{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformers in `sktime` <a class=\"anchor\" id=\"chapter3\"></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview of this notebook\n",
    "\n",
    "* why transformers? transformers in `sktime`\n",
    "\n",
    "    * transformers = modular data processing steps\n",
    "    * simple pipeline example & transformer explained"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* overview of transformer features\n",
    "\n",
    "    * types of transformers - input types, output types\n",
    "    * broadcasting/vectorization to panel, hierarchical, multivariate\n",
    "    * searching for transformers using `all_estimators`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "* [3. Transformers in sktime](#chapter3)\n",
    "    * [3.1 Wherefore transformers?](#section_3_1)\n",
    "    * [3.2 Transformers - interface and features](#section_3_2)\n",
    "        * [3.2.1 What are transformers?](#section_3_2_1)\n",
    "        * [3.2.2 Different types of transformers](#section_3_2_2)\n",
    "        * [3.2.3 Broadcasting aka vectorization of transformers](#section_3_2_3)         \n",
    "        * [3.2.4 Transformers as pipeline components](#section_3_2_4)\n",
    "    * [3.3 Combining transformers, feature engineering](#section_3_3) \n",
    "    * [3.4 Technical details - transformer types and signatures](#section_3_4)\n",
    "    * [3.5 Extension guide](#section_3_5)        \n",
    "    * [3.6 Summary](#section_3_6) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Wherefore transformers? <a class=\"anchor\" id=\"section_3_1\"></a>\n",
    "\n",
    "or: why sktime transformers will improve your life!\n",
    "\n",
    "(disclaimer: not the same product as deep learning transformers)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "suppose we want to forecast this well-known dataset\n",
    "(airline passengers by year in a fixed scope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>,\n",
       " <AxesSubplot: ylabel='Number of airline passengers'>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sktime.datasets import load_airline\n",
    "from sktime.utils.plotting import plot_series\n",
    "\n",
    "y = load_airline()\n",
    "plot_series(y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observations:\n",
    "\n",
    "* there is seasonal periodicity, 12 month period\n",
    "* seasonal periodicity looks multiplicative (not additive) to trend"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "idea: forecast might be easier\n",
    "\n",
    "* with seasonality removed\n",
    "* on logarithmic value scale (multiplication becomes addition)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive approach - don't do this at home!\n",
    "\n",
    "Maybe doing this manually step by step is a good idea?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>,\n",
       " <AxesSubplot: ylabel='Number of airline passengers'>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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USHl6LCZG+Jp9rEarQ/dl23G1WoU9s8djbGi3lg/qZDakZuG5n09hYGAXHJs/EQKB+QFgc8iVaqxNyUR8YlqTbUviorFgUoRVg6EdWUVFBby8vMyKr9k1gzIwMBD9+vVrdF/fvn1NlmPrbdmyBY899hi+/fZbk8FJAPD29kZ0dDQyMjKMbpfJZPD09Gx0IyIiIiIiIuro5Eo1Vu/IQHxiGspqVACAshoVViSmYc2ODGZStkJiWhHKalQI9JQhJsz88m4AEAkFmBxZP807vdgWy3NqOp0Om/ZdBAA8PjrE6sFJAJAIhdiQmmV02/rULEiEdg2ldVh2varjx4/H+fPnG92XlpaGkJAQk8d9/fXXmDlzJr7++mvcdNNNLT5OVVUVMjMzERgY2Kb1EhEREREREXUkDMZY33etLO/WmxJVH6BMYx/Ka+3NLsXpgkq4SoR4YHhPmzxGmUJlCNY32VajQrnC+DZqG7v+pJk3bx7279+PVatWISMjA1999RU2bdqEZ5991rDPokWL8NBDDxm+/uqrr/DQQw/h7bffxujRo5Gfn4/8/HyUl5cb9nnxxRexa9cuZGdnY+/evbjjjjsgEolw3333tevzIyIiIiIiInIExnpMarQ6FMuVDMZYkUKlwc+n8wEAMwa3LkkqLrquD+W+i6WoqmUGa0Ob9tdlT94zpAe8XSU2eQxvF0mz5/Z2lcDLxTaP29nZNUA5cuRI/PTTT/j6668xYMAAxMfHY926dbj//vsN++Tl5TUq+d60aRPUajWeffZZBAYGGm7PPfecYZ/c3Fzcd9996N27N2bMmAEfHx/s378ffn5+7fr8iIiIiIiIiOxNodJgbUomApYnIGBZ3cCPtSmZKK9RwctVzGCMFSWkFaFCoUYPLxeMC2ld/8hwH3eEdXODWqvDrswSK6/QeV2tVhqGDz05xnTlbVuotFrMjQkzum1uTBhUWg4vsgW7d/W8+eabcfPNNze7ffPmzY2+3rlzZ4vn3LJlSxtXRUREREREROT8jA38KKtRIT4xDTqdDrNG98KcmFDEJ6Y3OVYfjJHaN7fJqejLu/81KBDCVpR368VG++LD/TlISi/CTf26W2t5Tu3zw7moVWsxOMgTo3p52+xx3KViLJwcCaCuzYF+cNTs8aF4eXIkXDk4yib4U4aIiIiIiIiogzLVY3LjnmwEdnHBoslRWBIXbcik9HaVYElcNBZOjuS0YgvUqDT4pb68+54hPdp0rtiougrQZA7KAVA/HKe+vPuJMbYZjtOQi0SEBZMikL90KgqWTUXua7EY2sML7+0x/l6ituNPGiIiIiIiIqIOypyBH34eMiyYFIFFU6KQV6GAn4cUao0OLswUs8i2c4WoqtWgl7crRrcxw29ypC8EAuBUfiXyKhQI9HSxziKd1F8XruJcYRXcpSLcP6xtwV9z6YPzfh4yJJwvxF2fHYarRIh/D+vZ6b8ftsAMSiIiIiIiIqIOytyBH+5SMWRiIZ7+4QTCXk/GwUtl7bjKjmH/xavwdZfiX4MD25zh5+MuxbAeXgCYRQkAXx+9DAC4d2gPeNqhL2pctB/GhHRFjUqLVclN2yFQ27U5QKnRaHDs2DGUlpZaYz1EREREREREZCWWDvzwdZeiWK7E3uyr7bG8DkE/If2pcWHIenUKXrguwirnnVJf5p2UVmSV8zkb/XUtqKzFW7f2w4+PjMRzzbyWbU0gEGDl9D4A6iaJX7xabZd1dGQWByiff/55fPzxxwDqgpMTJ07EsGHDEBwcbNYAGyIiIiIiIiJqH+5SMeZPDMfi2CizekyOrZ88vS+bSUjmaDghPWJVMoLjk/C//RehUGnafO64aF8AQFJ6MXQ6XZvP50waXtfA5QkIjk/C37lliPR1t9uaJkf5YnKkL1QaHVYkpbV8AFnE4gDl999/j8GDBwMAfv31V2RlZeHcuXOYN28eXn31VasvkIiIiIiIiDoHfcZUYVUtlGot5Eq1vZfk9HQ6HR76+iiG9fTGlSVxKFg2FflLp2LBpAijPSbHhnYFAOzPKYVW27mCYpaSK9VYvSMD8Ylphj6fZTUqrEhMw5odGW1+/Y4P7QYXsRBKjRZZnShjr7nrujIp3SrXtS3ip/cGUDdRPK2oym7r6IgsDlAWFxcjICAAAPDHH3/g7rvvRnR0NB599FGcPHnS6gskIiIiIiKijsFUALJhxlTAsgQELE/AmymZVslE68xSs65i6+kC3P/lEShUGvh5yCAVC5udzj0woAvcpSJUKNQ4U1DZzqt1LqYmpK9PzYJE2Lauei4SERKeHIOsV6dAJhZ2mqC9ra9rW4wN7Yab+vpDo9VheQKzKK3J4u9q9+7dcebMGWg0Gmzbtg1xcXEAgOrqaohEnPBFRERERERETZkKQNo6E60ze39vNgDg38N6wttN2uL+YpEQo3vVZVHuvcgyb1PMmZDeFgqVBolpRQiOT0JwfFKHDNpf+6FFtUpt8+vaVvHT+6CPvwdmDA5CrVrDjG8rsThAOXPmTMyYMQMDBgyAQCBAbGwsAODAgQPo06eP1RdIREREREREzs1UAPK/f12AWChw2IwpZ5ZfocAPJ/MAAM+MCzX7OH2Z9z4OyjHJ3AnprfHPeya9wwbtjX1oseGvLHjZ8Lpaw5AeXtgzezyO5JYhcHkiM76txOKf8suWLcPHH3+MJ554Anv27IFMJgMAiEQiLFy40OoLJCIiIiIiIudmqmRz65kClNeoHTpjyll9dDAHKo0OY0O6YmgPL7OPGxtSH6BkBqVJKq0WcyyYkG4JRy5ztobmPrRY9Mc5nC+qstl1tQa5Uo11f13AyqSOGzy2B+NNJ5qhUqkwffp0fPDBB7jrrrsabXv44YetujAiIiIiIiLqGEyVbKYXyeHlWpcxZWwfR8iYckZqjRab9l0EADxtQfYkAIypD1CmFclRLK+Fr7vM2svrENylYjw/IQw6nQ4b92SjrEYFb1cJ5saEYeHkSKNDiMxlTpmzn4fzfl9MBWCf+O44Up4eBwHqgrHWvK7WULf2bKPb1qdm4ZUpUe27oA7CogClRCLBiRMnbLUWIiIiIiIi6oD0pbDGAi5qrQ5KjRZzY8KwIrHp0Al9xpTU8gLATu3XMwXILVfA112KuwcHWnRsNzcp+vp74GxhFfZll+KW/gE2WqVzyymtxo0fHcDKG/oib2kcKhRqeLlIoNJq2xxEM/We6QhBe1MB2IM5ZahRarBgUgRemRKFcoXKatfVGjp68NheLP4J/8ADD+Djjz+2xVqIiIiIiIioA1Jp6wKQxsyNCYNIACycHIklcdGG3nPerhIsjovCwsmRzU6cpubph+PMGt0LMrHlQZ2xod0AcFCOKe/uycaZgiq8uycLMrGoxQnplmjpPWPvMue2aql/p4dMDHepGFKx0KrX1Rps2Xu0M7P4u6tWq/HJJ58gKSkJw4cPh7u7e6Pt77zzjtUWR0RERERERM7PXSrG89eFQ9tCKaw+Y+pqjRJdZGLsyCiGUGDnxTuh84VVSEovhkAAPDkmpFXnGBvSFZ8czMH+bAYojalWqvHRgRwAaLZfYlu4S8VYODkSQOMy59njQx2izLmtVBotZo8Pxcqk9CbbHD1rWh88Zsa3dVkcoDx16hSGDRsGAEhLa/zNEAj4LwcRERERERE1dragEnd/frjFUlh9hpSfuwxD39mFU/mV+HnmSNzKEmOLvL8vGwBwc9/uCO3m1qpzjKuf5H3wUilUGi0kIgZcGvry78sorVEhrJsbburb3SaP4SIRGYL2pTUqeMhESDhfhNIaFQKdPED5V9ZVQ2DX2v07ba254LEzrN2RWRygTElJscU6iIiIiIiIqINatv08zhRU4fPDl3D7gAD4edT9Ad9clpFIKEBstB9O5Vdiy9HLDFBaoFqpxm+nCwBYPhynod5+HujqKkFpjQrHr1RgRLC3dRbYAeh0Oqz/q27Ay+yYUIhsmOarD9p37yLDHZ8exC+nC7Bsam8smRpts8e0NbVGi3m/nIJAIMA3Dw7H4thoh+sz2ZKGweO8yrper8VVSqdYu6Nq9UcgGRkZ2L59O2pqagDUvUGJiIiIiIiIGjp6uRzfnciDQAAsn9bb7OPuG9IDALD1dAHktWpbLa/DkCvVUKq1KKtR4/iLE7Ht8dGYGu3X6vMJhQKMrZ/mvTf7qrWW2SGkZJTgdEEl3KUizBzZq90e91+DgwAAnxzMgUbrvDGYzw7n4nyRHMVyJUK7ujlkn0lz6Htkfn44F2GvJ2NNSoa9l+TULA5QlpSUYMqUKYiOjsaNN96IvLw8AMCsWbPwwgsvWH2BRERERERE5LyWbDsHALh3SA8MDPQ0+7gRwV6I8HFDtUqDrWcKbLW8DkGh0mBtSiYCliegZ3wiguOTsCf7KpSatg1SGVNf5r2fg3IaWZ96AQDw8IjgZoel2MKdAwPh7SpBTlkNktOL2u1xrUmh0mB5wnkAwKIpkeji4jwByeYM7+mFYrkSv58psCh5T/+hQmFVLZRqLeTKzv1BjMUBynnz5kEikSAnJwdubv/0srjnnnuwbds2qy6OiIiIiIiInNe+7Kv4/WwhREIBlllYkioQCHBPfRbllqOXbbG8DkGuVGP1jgzEJ6ahrEYFACirUSE+MR1rdmS0KegxLqR+kjcH5RhcKJHj1/qA+eyY0HZ9bFeJCPcPq3tPfFw/oMfZvL8vG7nlCgR7u+DpsaH2Xo5VTI70hZtEhNxyBY5dqTDrmIYfKgQsS0DA8gS8mZIJhUpj49U6LosDlAkJCXjjjTfQs2fPRvdHRUXh4sWLVlsYERERERERObfFf9ZlTz48IhhRfh4WH3/f0LpgzLbzhbharbTq2joKiVCIDalZRretT82CRNj64TajenlDKAByymqQW1bT6vN0JBv3ZEOnA6b19kMf/y7t/viPja4rKf/5dD6Kqmrb/fHbolKhxurkujLoJXG9O0y/RheJCFN717VT+PV0y9nezX2osCIxrc0fKjgzi39SyeXyRpmTelevXoVMJrPKooiIiIiIiMi5HbhYipP5lZCKhFgSF9Wqc/QP6IKBgV2g0ujw48k8K6+wYyhTqAxBjibbalQoVxjfZg4PmRiDg+rK8vexzBtVtWp8crAuc3Fu/QTq9jY4yAsjenpBpdHhiyO5dllDa72zOxPFciV6+7nj4RE9Wz7Aidzcr26S+29n8lvc15YfKjgzi5/1hAkT8Pnnnxu+FggE0Gq1WLt2LSZNmmTVxREREREREZFz0fdVC/CUIevVKdg7Zzx6dW2a5GKuew1l3lestcQOxdtF0mwfRG9XCbxc2tYjcUx9mTcDlMCvZ/IhFQkR7eeOab397baOWfVZlB8fyHGagcWl1Up8dqguoLpieh+IRR0rCHdT3+4QCIDDueW4Uq4wua8tP1RwZha/ItauXYtNmzbhhhtugFKpxEsvvYQBAwZg9+7deOONN2yxRiIiIiIiInICDfuqhb2ejOD4JGw9XdCmvmr6AGVKZjHyKkz/4d8ZqbTaZrP55saEQaVt26CccfWDcvZ14kne+qD7uJBuyHp1Cn5+ZCSEQoHd1nPf0B5wk4hwtrDK4QPH+msnV2pwcsFEJD05FncNDLT3sqyuexcZRgV7AwB+P2u6zNvWHyo4K4sDlAMGDEBaWhpiYmJw2223QS6X484778TRo0cRERFhizUSERERERGRg7NVX7UwHzeMCekKnQ749jizKK/lLhVj4eRILI6NMgQ9vF0lWBIXjYWTI+EubduUZP2gnL8vl6OmEw7waBR0X1UXdN9y7Ipdh5l4ukgwY3AQAODjg7YdltOWSdMNr12vlUkIjk/CrgslbZ4u76j+KfM2HaBUabWY08yAJWt8qOCsBDpnyQduRxUVFfDy8kJ5eTk8PT3tvRwiIiIiIrIyuVINiVCIMoUK3i4SqLTaNgdyOjulWouA5QlGSxe9XSXIXzoVUnHryjrX/3UBz/9yGqN7eWPf3AltXWqHU1RViz3ZpYiL9oVcqbHqa1qn06HHikTkV9Zi97PjEBPmY4UVOwe5Uo21KZmIT0xrsm1JXDQWTIqw28+NPVlXMeHdPXCTiHBlaRw8bZB1p1BpsHpHBjakZqGsRgVvVwnmxoRh4eTIFgfcOPK1s5UTVyow5J1dcBELUbxiGtyaeX764UYbUrOwcU+2xdfWmVgSX7P41XDixAmj9wsEAri4uKBXr14clkNERERERA5Ln9XTmj+6qXnm9FXz82jd34ozBgdh/tbTOJBThqySaoT5tL6nZUf0x9lCzPzmGKZE+SLxybEAAKnlBZNGCQQCjA3pip9O5WNfdmmnClC2NMzklSmtG/5kDeNCu6KPvwfOFVbhm2NX8PiYEKue31iAUZ8RDaDFAKMjXztbGRjYBb28XZFTVoPk9GLc0j/A6H5rUzLx+9kCvHvnQCyOjUa5QgWv+g8VOvO/QRb/xBoyZAiGDh2KoUOHYsiQIYavhwwZgj59+sDLywsPP/wwFAr2BiEiIiIiIsdiqzJksm1ftQBPF0yK9AUAbDl2udXn6agS0ooAAKN6edvk/GNDO+egHEceZiIQCDBrVN2wnB9OWH/CfVsnTTvytbMVgUBgKPP+tZky76KqWry/NxvnCqugUGkgFQvh5yGDVCzscBmllrI4QPnTTz8hKioKmzZtwvHjx3H8+HFs2rQJvXv3xldffYWPP/4YO3bswOLFi22xXiIiIiIiolYz94/utvRd66zq+qrZbliLfljO9vOFbTpPR6PR6pBQf02m22iytH5QzvnCSqeZGt1Qa9/PXg4+zOSRkcH4ZeZI/PDICORXKlr1s8rYtdHpdCiuVrYpwNhZB8Hc0r8uQPn72QJotU3fK2/vuoBqlQYjenpheh/7TYJ3RBYHKF9//XX897//xaxZszBw4EAMHDgQs2bNwn/+8x+8/fbbuP/++7Fhwwb89NNPZp3v8uXLeOCBB+Dj4wNXV1cMHDgQhw8fNnnMzp07MWzYMMhkMkRGRmLz5s1N9nn33XcRGhoKFxcXjB49GgcPHrT0qRIRERERUQfTUlZPVa260WCHgGUJCFiegDdTMu06FMMZuEvFmH9duM2GtfxrUAB+mTkSvz82utXBmI7o79xylFSr0EUmxpiQrjZ5jOE9vPDLzJE4+Px1The0b+37WV6rxt6sq5g9PtTodkcYZuIuFeHQpTIExychaHmixT+rmrs2ZTUqeLmI2xRgvFKhcOhrZyvXR/jAQyZCXkUt/r5c3mhbsbwW7+6p+4DstbhoCAT2mwTviCz+F+LkyZMICWna2yAkJAQnT54EUFcGnpfXcopxaWkpxo8fj0mTJuHPP/+En58f0tPT0bVr8z9Us7KycNNNN+Gpp57Cl19+ieTkZDz22GMIDAzEtGnTAADffPMN5s+fjw8++ACjR4/GunXrMG3aNJw/fx7+/oxQExERERF1VvqsnuYGuYhFAqzakY6ViemG+y3pu9bZPbLlGB4eGYwrS+JQWau2al81mbguGPPwlmPsHdrA9rS67MkpUb6QiKzTd/JaOsApr31r+yhqtDo88NXfOF8kx+5nx0EoEGC9g/Ws1T+3lUmt+1ll6tpodTo8OqoX5sSEIr7Bz0K9OTGhUGm1zfY5vVxeg/v+7wi2PjoKAgGwIbVjD4JpSCYWYWq0H348mY9fzxRgRLC3Ydu63VmQKzUYEuRpKAWnf1g8xXvo0KEYPHgwNm3aBKlUCgBQqVR4/PHHcfz4cRw9ehR79uzBAw88gKws46UTegsXLsSePXvw119/mf34L7/8Mn7//XecOnXKcN+9996LsrIybNu2DQAwevRojBw5Ehs3bgQAaLVaBAcHY86cOVi4cGGLj8Ep3kREREREHVPdH+UZRv/oXnVDHzx/XTiCViTaZBJ1R3eprAYhK5MgFACFy6ahm7vUaufujBOBzXXdu3uQmnUV7905EE+NC7X6+Z352rd2svwLW0/jP7svQCYWYu+c8Yj284BEKGw0zMTez7m1z82S47U6HdbsyGgUnJ09PhRzYsJQoVAjwte9ybEarQ6xH+zDrgsluGtgAD65dyhkIse6dra2+dAlPPrNMQzt4Ykj8yYCAEqrlQh9PRmVtWr88PAI3DEw0M6rbB+WxNcs/pf13XffxW+//YaePXsiNjYWsbGx6NmzJ3777Te8//77AIALFy7gmWeeafFcW7duxYgRI3D33XfD398fQ4cOxYcffmjymH379iE2NrbRfdOmTcO+ffsAAEqlEkeOHGm0j1AoRGxsrGGfa9XW1qKioqLRjYiIiIiIOh6JUIg5McbLkOddF47Sms432MFa/jhbNxRiTEhXqwYngbYP7OioymtUhsE102zUf9KZr31LLR3K6t/PDfswKlQaTAj3QR9/D3x6zxAM7eENd6nY4YaZtHUIjTnHu0hEWDApAvlLp6Jg2VTkL52KKVG+mPjeXtzyyUFUKpqW+a/ekY5dF0rgIRNh1Y190UXmeNfO1m7q6w+BADh6uQK5ZTUAgP/+lYXKWjUGBnbBbc1M9+7sLH5ljBs3DllZWfjyyy+Rllb3Ccrdd9+Nf//73+jSpQsA4MEHHzTrXBcuXMD777+P+fPn45VXXsGhQ4cwd+5cSKVSPPzww0aPyc/PR/fujVNhu3fvjoqKCtTU1KC0tBQajcboPufOnTN6ztWrV2P58uVmrZmIiIiIiJzX5sOXsG73Bbx9Sz8sjo1ulNUjk4jgIxCYLAHvqIMdrOHPc3Wlxjf2tX7pojnBFD8PmdUf19HtyCiGRqtDtJ87wnzcbPIYznztW2rp4CEVoUKhwtu7LmDDNVmC++bGOPT7vaXn1tLazT1eH1DUf4/7+HdBZa0al8sVePSbY/j2oeGGXooHLpZieUJdnOjdOwciys+j9U/Qifl5yDA2pCv2ZpciKa0Idw0KxP8dyQUALI6NhlDI3pPGtOqjji5duuCpp57CO++8g3feeQdPPvmkIThpCa1Wi2HDhmHVqlUYOnQonnjiCTz++OP44IMPWrOsVlu0aBHKy8sNt0uXLrXr4xMRERERke3VqjVYlZSOc4VVSCuWG83qUWm1mGvDSdQdlUKlQVJaMQDgRhtMpu2sE4Fbsq1+evdUG2VPAs597U29n2ePD0VhlRJv7awrX9cH6spqVFiZlI7/7Lrg0IOA2vqzqrXHd+8iw7cPDodEJMDpgkqcLayCUq1FQWUtBgR2wXcPjcBL10fgweHBlj2hDubhET3x0yMjMWNID1TUqnH8xYlIeGIM7uokpd2t0arc2vT0dKSkpKCwsBDaa160S5YsMfs8gYGB6NevX6P7+vbtix9++KHZYwICAlBQUNDovoKCAnh6esLV1RUikQgikcjoPgEBxtNoZTIZZDLH/MSHiIiIiIis45ODl5BTVoMgTxc8Mabp4E+gLlto4eRIAHC4oRiObPeFElSrNAjydMHgIOv38dcHU1YY6YOoD6Y0N7Cjo9LpdEg4XwQAmBbtZ7PHceZr7y4V4+XJkdDqdNi4p/GglpcnR0IAYOOebKPHrk/NwitTotp1vZZo7mfV7PGheHlSJFylpn9WuUvFmD8x3Oi1aeln3djQbth8z1DERvtiQ2pWo+Nnjw/F0mm9rfpcndH9w3pizY4MzPzmn8FSc2JCERPWDS5C/jtijMUByg8//BBPP/00fH19ERAQ0GgsukAgsChAOX78eJw/f77RfWlpaUanhOuNHTsWf/zxR6P7EhMTMXbsWACAVCrF8OHDkZycjNtvvx1AXaZmcnIyZs+ebfbaiIiIiIio41CoNFiVXDcYZ9GUSLia+ONb33ftlSlRyK9UwMddiktlCgYnTfj9bF0m3w19/Rv9jWgtzQVj5sSEdtrAcVqRHBdLayAVCXF9hI/NHsfZr/3fuWUY1tMbl16LRbVSY2jp4CoRobCq1mnL14HGP6vKFSq4S8XYfr4QHx64iLkTwk0eq9ZoccenhzBnQjiuLIlDZa3acG3M+Z7eOqA73tiR0WSK+MqkdAgFAocenmRrzU1Yj09MhwCd+9qYYvEVWblyJV5//XW8/PLLbX7wefPmYdy4cVi1ahVmzJiBgwcPYtOmTdi0aZNhn0WLFuHy5cv4/PPPAQBPPfUUNm7ciJdeegmPPvooduzYgW+//Ra///674Zj58+fj4YcfxogRIzBq1CisW7cOcrkcM2fObPOaiYiIiIjI+Xx4IAeXyxXo6eWCx0b3anF//R+POzNL8OKvZxDWzQ0Hnptg62U6LX3/yZv62q7UuGEwpqRaCU8XMf66cBWyTjpVfXt9efeE8G5wl9k22GHs2u/MKIEzdNL79ngeNqRmYe6EMKy7bQAAGDI+29rH0RE07BH5y6l83PXZYfi5S/Hk2BDIxM0HGr8/kYeUzBKcLqhE9quxhkCsudmwEqHQabNPba2lwVKd+dqYYvFP8tLSUtx9991WefCRI0fip59+wtdff40BAwYgPj4e69atw/3332/YJy8vDzk5OYavw8LC8PvvvyMxMRGDBw/G22+/jY8++gjTpk0z7HPPPffgrbfewpIlSzBkyBAcO3YM27ZtazI4h4iIiIiIOr4alQar67MnX42NMvlH+7Vu6OOPshoVDl0qw8m8Clst0amlFVUho1gOiUiAKZG2KzUGYJim7OUiRr+1KbjxowP4+3K5TR/TUenLu6fasLy7IcMka3cpYjam4pZPDmLLsSvt8titpdPpDNPlJ4Y3zTLtaD1nb+zrj55eLiiSK/GNie+NTqfDO7syAQDPjAtrVRZsW6eId2S8Nq1jcYDy7rvvRkJCgtUWcPPNN+PkyZNQKBQ4e/YsHn/88UbbN2/ejJ07dza67/rrr8fRo0dRW1uLzMxMPPLII03OO3v2bFy8eBG1tbU4cOAARo8ebbU1ExERERGR8/hgXzbyK2sR0tUVM0e2nD3ZkJ+HDLf2r0t0+PQQh2ka83uDAFAXl/YpW3STijG6V1cAdZlgnY1CpUFKZt1Qomk2HJBjjFgkxIzBPQAAG/dkQafTtevjWyKtSI7MkmpIRULERjUN5OrL15fERRsGAXm7SrAkLhoLJ0c6XRmuRCTE0+NCAQAbUpv/3vx14SoO55bDRSzE0+Oab7FnijMPT7I1XpvWsThAGRkZiddeew2PPPII3n77baxfv77RjYiIiIiIyFFUK9X4aP9FAMDi2GhIW1EOrA9q/t+RXCjVzpVR1R7+bNB/sj39a1AQAOD741ccOkhmC6lZV1Gj0iLQU4aBgV3a/fEfH9MLMrEQR3LLsf9iabs/vrkMwfOIbs0Gz/Xl6/lLp6Jg2VTkL52KBZMiHL63ZnMafm/2NfO9+c/uuuzJB0f0bHWPzY6WfWpNvDatY/HHAZs2bYKHhwd27dqFXbt2NdomEAgwd+5cqy2OiIiIiIioNeRKNSRCIUprVDj4/HVIzbqKyZG+rTrXtN5+CPSUIa+iFr+dLcCdAwOtvFrnValQY9eFEgDATX3bt6XWjX394SIWIrOkGseuVGBoD692fXx72m6Y3m2boUQt8XWX4b6hPbD50CVs3JONsaHd2n0N5vijPnh+YwuvzYZ9HAHz+zA6Il93Gf49rAc+PXgJG1KzMO6a7016URW2nqkL3M67zvQgHVOaG55kzhTwjo7XpnUsDlBmZRlv9ElEREREROQIFCoN1qZkYsM1E4cnhvugNX8XikVCPDg8GGtTMvDpwRwGKBtIziiCSqNDhI8bonzd2/WxPWRi3NjXHz+ezMd3x690qgBlQlpd4G1q7/bpP2nMnJgwbD50Cd8dv4I3b+6HIC8Xu63FmPIaFXbbKXhub3PGh+HTg5fw/Yk8vFlWg57eroZt/9l9ATodcHPf7ujj37bs22uniFsyBbyj47WxXKs/FlAqlTh//jzUarU110NERERERNRqcqUaq3dkID4xzTCkoKxGhfjEdKzZkQG5snV/v8wcGQygblr1lXKF1dbr7H5vkKFmj0w+Q5n3ibxOU+Z9ubwGJ/MqIRAAce00IMeYoT28MD60K9RaHf5X30bBkSSmFUGt1SHazx2R7Rw8t7chPbxwXXg3aLQ6fLDvn+9NiVyJzw7X9dKdP7H12ZMNGYYnecggFQudrm+nLfHaWMbiAGV1dTVmzZoFNzc39O/f3zBhe86cOVizZo3VF0hERERERGQuiVCIDanGq77Wp2ZBImxdjkZvfw+MD+0KrQ744khuW5bYYeh0OkP/yRvbuf+k3s39usNFLERGsRwnOsmUdf307pE9veHjLrXrWmbX99nbtP+iw/VnNbe8u6Oa0+B7o1BpAADv78tGjUqLYT28MDGi6VRzInuy+F/nRYsW4fjx49i5cydcXP5J4Y6NjcU333xj1cUREREREXVkcqUaSrUWhVW1UKq1rc7uo3+UKVSGzMkm22pUKFcY32aOmaPqhuV8eiin02TrmXL8SgWuVCjgJhFhYrh9gh0eMjFu6FMXHP3ueOeY5n22sBK+7lK7lnfr3TkwEEGeLiiorMV3J67YezkGWq0Of5yr67N4k52C5/Z2W/8ABHu7oFiuxE+n8qFUa7Dl6GUAwLyJ4XbJeCYyxeIA5c8//4yNGzciJiam0Qu6f//+yMzMtOriiIiIiIg6Kn2fxIDlCQhYloCA5Ql4MyXTkOlCrePtIoG3q8T4NlcJvFyMbzPH3YOC4C4VIa1Ijr3Zjju5uL3oJyRPifK1a1+1uwbV9QT9/oTjT/Nuy4cS+mOfGReGrFen4MmxITZcqXkkIiGeGle3jo3NZC7bw5HcchRWKdFFJsaEsM6ZKSgWCfFabDR+emQkbuvfHcVyFQ48NwF/PDYKMwYH2Xt5RE1YHKAsKiqCv3/TTyDkcjkj8EREREREZmiuT+KKxLQ29UkkQKXVYm59aeO15saEQaVtfRlqFxcx7q7vefjJoZxWn6ej+POcfcu79W7pFwCZWIi0IjlO5lXadS2mtOVDiYbHhq9KRnB8Ej7cn+MQH2g8MToEAwM9sXByFGrVGptkhFsa2NUHz+OifSEVO+9E7ra6f3hPHMktQ8/4JPSMT0RwfBL2XSyFRuvYgXzqnCx+p44YMQK///674Wt9UPKjjz7C2LFjrbcyIiIiIqIOylZ9EqluKMGCSRFYHBtlyKT0dpVgSVw0Fk6ObPOQgpmj6oblfHf8CqpqO28g+apciapaNXzdpYYSa3vp4iLG9Ppy5+8dqMy4obZ8KOHoH2j4d5Fh5zPjcCS3DIHLE62eEd6awO4fZ/Xl3Z2z/yRQ97pZsyMDK5PSrTowjMhWLP7XedWqVbjhhhtw5swZqNVq/Pe//8WZM2ewd+9e7Nq1yxZrJCIiIiLqUMzpk+jnIWvnVXUc7+/NxrCe3sh9LQ5ypRpeLhKotFqrlCHHhHXDtN5+eGpsKMRCAQqrauFdf35nmdAqV6ohEQpRplC1au1ypRpuUhF+mjkK3T2kcIRcrH8NDsIvpwvw/Yk8LJ/W2+Gq+1r6UOKVKVE2ObY9yJVqrNudiZVJ6Yb79AFUAFgwKaLV7w25Uo21KZmIrz+XOefOq1DgcG45ANg9eG5Pjv66IbqWxR/NxsTE4NixY1Cr1Rg4cCASEhLg7++Pffv2Yfjw4bZYIxERERFRh2LLPomdnU6nw4f7c3Dn5kNIPF8EPw8ZpGKh1YKHAoEA3zw4HEdyyxC0wvrZYrbW1t6n+uODViQiYlUyesYnOcRzv7lvd0hFQpwrrMLpfMcr827L8CZbDn6yhrpAWLbRbW3NCG9Ntrm+9cCInl4I8HRpsr2zcPTXDdG1WvWvdEREBD788ENrr4WIiIiIqFPQ90lc0SArSG/2+FDkVSoQ0tXNDitzfucKq5BeLIdUJMSkKOsPx5Ar1Xh71wWbZIvZWmuy0ax5vC15uUowrbcffj1TgO9O5GFAoKdd1tEc/YcSxgJGpj6UUGm06CITt+rY9mLLjPDWnFtf3n1jJy7vBlr/miOyF4s/yvj7779x8uRJw9e//PILbr/9drzyyitQKpVWXRwRERERUUfkLhXj5cmRTfokLo6NwpyYMNz6yUH84KC99BzdL6fzAQCTI33gaYM/wJ25f6i5a29uIImjP/d/Da6b5p2cVmTXdRij0moxJybU6LbZ40NxubymyXUvq1HhiW+PI+F8EWaPN35sWwc/WYMtM8ItPbdSrUViWjGAzt1/ErDtwDAiW7D4460nn3wSCxcuxMCBA3HhwgXcc889uPPOO/Hdd9+huroa69ats8EyiYiIiIg6luT0Igzr6Y1Lr8WiWqmBl4sESo0WS7efx8m8Sizdfh6DAz3Rq6tbq3sFdkZbT9dlT93aP8Am53fm/qEtrb2sRoUuLmKsTcnEhtQslNWo4O0qwdyYMCy4PhxXq9UO/dxv7x+AX2aOxOQoX+RXKtDNVeow7xmJUIA5MWHQ6YCNe7IN13b2+FA8PyEcOgBv7Mhosm3tLf3w9A8nsPneoRAKBFh/zfdl4eRIq/RWbQtTGeH6QJjU8twoAEC1SoPZ40MbZSzrzR4fiuzSakT7eRju+yurBJW1anTvIsPwnl6tesyOwl0qxsLJkQDgkK8bomtZ/JM6LS0NQ4YMAQB89913mDhxIr766ivs2bMH9957LwOURERERERm2LQvB7+dLcCqG/pgYf2wAqlYiLU394OrRIjnJoRjQ2pWo4AF/7A0La9Cgf0XSwHYLkDpzGWTLa3dRSLE6uR0o+XrLmIhnr8u3KGfu0QkxKFLZXh4yzGHe8/8fKoAyxLO451b+2FxbDTKFSrD8KYr5Qp8cSS3yXXXf/3unQPhIaubTv/KlKhGx9r7eQG2C4RptTos/P0M4qf3AdA4sDsnJhSzx4fh+vf34qXrI/HwyGAAwN+55XWT5Xv7Qyh0rEFJ9uAiETns64boWhYHKHU6HbT1qcBJSUm4+eabAQDBwcEoLi627uqIiIiIiDqgq9VKbE+rG+Rw24DGgTSRUIBFk6PwRkqGU/Y5tKdfz9RlT44K9kaQl22GY9gyW8zWTK395UkRkIqF2Lgn2+ixa3dm4tnxoQ773PX9MR31PfPxwYs4V1iFPdmlmN6nuyHTVAohQrq6NXvdN+7JxuLYaAAwrL/hsY6iYSDsSoUCfh5SlFar2hQIW5OSgU37c3DgYhm2PTG6SWD3P7sv4GxBFdbuzEBMWDcEe7tixuAgPDM+FAWVtVZ8ds7NkV83RA1Z/MocMWIEVq5ciS+++AK7du3CTTfdBADIyspC9+6du8cDEREREZE5fjiRB5VGh8FBnujbvUuT7RJR84EiR+j156h+OVXXf/LaoK816bPFlsRFN+ofuiQuGgsnRzp04NhdKsYL10c06X26JC4az08IR4XCdAm3SqNz2OfeXv0xm+vPaUr21Wokpdcl88ysz/RrqKNMW3aXiiEVC/HxgRyEvZ6M1TsyWn2ulIxiLNl2DgAwZ0IYundxgVQshJ+HDFKxEO5SMV6dEoWlU6Ox8+lx+OzwJQQsT0DYqmQExyfh88O5dp8sT0SWsfhfkHXr1uH+++/Hzz//jFdffRWRkXWp3N9//z3GjRtn9QUSEREREXU0Xx+9DAC4d0gPo9uduc+hvVQq1EiuDwLdZqPybj1j2WJlNW3LFmsvq5PTMKpXN1xeEoeqWrUhG00mEcFbIDBZwu0hqwtAOWLJaHu8ZxQqjdH+nC2VMX966BJ0OmBKlC/CfdybbHfmtgHGxIR3w+vJ6fj++BX897b+EItaDg7LlWpIhEKU1b+mFCoNov08MKZXVzw6qpfRYwQCAV6cGMFsc6IOwuJ36qBBgxpN8dZ78803IRI5/j/IRERERET2dLm8BrsulAAA7h0SZHSfjhawaA/bzhdCqdEiytcdfbt7tHxAG+mDHpv2ZeOjg5dw39Ae+O/tA2z+uG2h0erw8YFLWLMjE7ufGYeYcB8A/5R8mlu+7oglo9Z6zzQMlDUcTKUvIY9vcG3MCYRptDpsPpQDAM0G2py5bYAxkyN94esuRZFciZTMEsRF+5nc31jgd/b4UPz17Hi4SUw/75ayzV+p7+9LRI7P4p9yly5dQm5uruHrgwcP4vnnn8fnn38OiYS/KBERERERmfLt8SvQ6YDxoV0R0s3N6D76gIUx+oAFNbb1dF159639AyAQtN9wjHFhPiiWK/H9iSvQaHXt9ritcTCnFEVyJbxcxBgd0rXJdmcuX7fGe0YfKAtYnoCAZQkIWJ6AN1MyIa9VQyQQtKqEPDGtCJfKFOjqKsEdzbQecObrboxEJMRdgwIBAFuOXTa5r1ypxuodGYhPTDMEl/UDgjakZqGl71pHKY8nolZkUP773//GE088gQcffBD5+fmIi4tD//798eWXXyI/Px9LliyxxTqJiIiIiDqELUevAADuHWq8vBtofiru7PGheGlSJNykrFxqSKXR4vezdUOHbrdh/0ljpkb7wdtVgryKWqRmlWBihG+7Pr4l9EOEbujjD0kzZbfOOvXX1HvGnEnSpjIkfd2luGtQYKtKyD85WJc9ef/wnibX4KzXvTn3DA7C//ZdxE8n8/H+nVpIxcZfby31Dm0pA5LZ5kQdh8UZlKdOncKoUaMAAN9++y0GDBiAvXv34ssvv8TmzZutvT4iIiIiog4jo1iOQ5fKIBIKcPcg4+XdevqARf7SqShYNhW5r8VhaA8vrN6RbvK4zmj3hRKU1ajg5y7FGCOZgbYkFQsNQdFvjl1p18e21G/1Acqb+pkebqofdtJwIIkzaPieyVs6FZdei8XQHl4olitbPNZUoOy/f12Aj5vUkN14reYCYUVVtfilPrP3sWbKuxty1utuzIRwHwR6ylBWo0JCWlGz+7U1A5LZ5kQdh8UBSpVKBZms7pOhpKQk3HrrrQCAPn36IC8vz7qrIyIiIiLqQPTljlMifeHfpeWBHQ0DFifzKnDXZ4exNiUDl8trbL1Up/LL6brA2y39AyAStl95t9499b1EfziRB7XGMQMi2VercSq/EiKhADf08bf3cmxG/57p3kWGR7ccw12fHcb/9l9s8ThTgbLMkmrIVWqLA2FfHMmFSqPDiJ5eGBTkadkTcXIioQB3D657X3xjosxbnwFpdJsZGZAdrTyeqDOzOEDZv39/fPDBB/jrr7+QmJiI6dOnAwCuXLkCHx8fqy+QiIiIiKgj0Ol0+Prv+undJsq7mzM6pCsmhHWDSqPDO7suWHt5Tkun0+GXU3WJErf1N50ZaCuTI33h4yZBkVxpGIDkaPTZk+NDu6Kbm9TOq2kfd9VnKW8+dKnFwHFLgTJ3ifFA2OLYKMyJCYPkmsC4TqfDxwfqyrtnjW45e7IjundI3c+5X07no1qpNrqPNTIgr802z186FQsmRThteTxRZ2VxgPKNN97A//73P1x//fW47777MHjwYADA1q1bDaXfRERERETU2Im8CpwtrIJMLGx2WEZL9D32Nu2/iBIzylY7g2NXKnCpTAE3iQixLUwLthWJSIg7BtYNBXHUMm99gPLmfu3bo9OebhvQHT5uElwuV2D7+ebLjIG6QNns8aFGt+kDZdcGwvKWTsWoXt6Y8O4evPDrmUbH7L9YirOFVXCVCA2Bus5mdC9vhHR1RVWtBn/U94i9lptEhLkTwrA4NqpNGZAdqTyeqLOyOEB5/fXXo7i4GMXFxfjkk08M9z/xxBP44IMPrLo4IiIiIqKO4uv64Tg39fWHVzOZWi2Z3scfQ4I8IVdqmu2X19nszb4KX3cppvX2g6sdM6b0Zd4/nsyDysHKvCsVauzMrMvsvKWF/pMdiUwswoMjggEAH9cPq2nOkUtlmBPTcqCsYSBMJhZCKBDgXGEV3t2Tje+O/xOc/vN8IXzdpZgxOKjV73dnJxAIcE99cPbb48YD9z+cyEPMxj0YEeyNPGZAEnVqFgcoAUAkEqFr18bNp0NDQ+Hv33F7mRARERERtZZOpzP0YWtLNpVAIMDL9VmUG1KzUFVrvGyyM5Ar1VCqtbi5X3dkvToFa27qa9f1TAz3gb+HFFerVUhOL7brWq6VkFYIpUaLSF93RPu523s57WpW/XCaX88UIL9CYXQfrVaHeVtPY+J7e3HnwECLSoVv7NsdL02qe0+uTclAUVUtlGotZo7shaxXp2DlDX2s/6SciD5w/9uZAlQqGv+8qqpVY97W0zhXWIUjueWQMQOSqFNrVYDy+++/x4wZMzBmzBgMGzas0Y2IiIiIiBo7fqUCcqUGXWTiFicot+Rfg4IQ6euO0hoVNpkx/MNR6QOMhfUBHXkzPeqMUag0WJuSiYDlCQh7PRnB8Un48u/LUKg0NlyxaWKR0NDz8FsHK/P+p7y7OwSC9h8iZE/9A7pgbEhXaLQ6fHY41+g+P57Mw9HLFbhcrkBPbxeLS4VXTu+Nfw8Nwh+PjcaG1CwELE9AxKq61+WH+3Ps+rq0tyFBnoj2c4dCrcXWM/mNtsUnpuFyuQJh3dwM7SuIqPOyOEC5fv16zJw5E927d8fRo0cxatQo+Pj44MKFC7jhhhssOteyZcsgEAga3fr0af4Tpuuvv77J/gKBADfddJNhn0ceeaTJdv0gHyIiIiKi9qQPwvm6S5H16hQkPDmmzWXIIqEAL02KAAC8s+sCatXOF/xoGGAMWJaAgOUJeDMl06xAjlypxuodGYhPTDNMXS6rUWFFYhrW7MiwKNBpbffUTy3+6VQelGrblXlbEtzVaHX4vb7/X2cq725IP6Tm44M50Ol0jbZptDos3X4eAPD8deHwdZdZfH6xSIj1tw/EhtQsrExKd7jXpT01LPP+5ug/gfuzBZX4z+66YV//vX2AXdszEJFjsDhA+d5772HTpk3YsGEDpFIpXnrpJSQmJmLu3LkoLy+3eAH9+/dHXl6e4Zaamtrsvj/++GOjfU+dOgWRSIS777670X7Tp09vtN/XX39t8bqIiIiIiNqiYRCu18okBMcn4Y+zhVbJpnpweE9cF+6Dd+8cCJ0OrcpCtBdzA4zNBeEkQmGz/TfXp2ZBImxVkZhVjA/rhkBPGcoVaiSkmR7K0lqWBncP5pSiWK6El4sYMWHdbLImRzdjcBA8ZCJkFMux+5op61/+nYuzhVXo6irB/OvCW/0YHjIxNu7JNrrN3q9Le9MH7renFaK0WgmdTodX/zgLtVaHW/p1x82dNHBORI1Z3NghJycH48aNAwC4urqisrISAPDggw9izJgx2Lhxo2ULEIsREGDeJLlu3Rr/g7plyxa4ubk1CVDKZDKzz0lEREREZG1ypRprUzIRn5hmuK+sRoX4xDQIACyYFNGmHmsysQhbHx2Jt3ZmYuY3x1BWo4K3qwRzY8KwcHKkQw+XaCnA+MqUSNTUB+E2pGY1em4LJkWgtFplCGxeq6xGhXKFCn4elmfBWYNIKMC/BgVhQ2oWvj122eqBl+ZeVyvqvzb2uvq1vrx7eh9/SESdM0jmIRPj3iE98NGBHHxy8BImRvgCAJRqLZYn1F27lyZFtmmYTZnCcV+X9tYvoAtu6x+AR0YGw1UiQl5FLb64fxiS04sxrIeXvZdHRA7C4n+hAgICcPXqVQBAr169sH//fgBAVlZWk3R5c6SnpyMoKAjh4eG4//77kZNjerpaQx9//DHuvfdeuLs3bvS8c+dO+Pv7o3fv3nj66adRUlLSzBnq1NbWoqKiotGNiIiIiKi1bJ3lJ1eq8fauC05ZTtpSIKeyVoPVyelGMyw3/JUFX3epYcrytbxdJfByse/EZP1QkF9OF1i992BrXlcN+092Zo/Vl3l/d/yK4XX1ycEcZF2tRvcuMsweH9qm83u7SBz6dWlvm+8bgiO5ZQhakYie8YkIjk/Ckdwy+LpL7b00InIQFv9mNHnyZGzduhUAMHPmTMybNw9xcXG45557cMcdd1h0rtGjR2Pz5s3Ytm0b3n//fWRlZWHChAmGrExTDh48iFOnTuGxxx5rdP/06dPx+eefIzk5GW+88QZ27dqFG264ARpN878crF69Gl5eXoZbcHCwRc+DiIiIiKghc7Kp2sKRy5xbYiqQE+Hjhi4mSmXX7syESqPFnJgwo9vnxoRBpbVd70dzjOnVFZMifPD5fUMBtK783lh5u06nQ0m10qLXVfbVapzKr4RIKMANffxb/6Q6gJHB3hgY2AUKtRY/nsiDQqXBe3uzAQCvTImCu6xtU6NVWi3mOvDr0p7kSjXeMfKBSnxiusN/oEJE7UegszDtUavVQqvVQiyu+wG+ZcsW7N27F1FRUXjyySchlbb+E5CysjKEhITgnXfewaxZs0zu++STT2Lfvn04ceKEyf0uXLiAiIgIJCUlYcqUKUb3qa2tRW1treHriooKBAcHo7y8HJ6enpY/ESIiIiLq1JRqLQKWJxgNJnm7SpC/dCqk4tYHEQurahGwLKHZ7QXLpjpsOalcqcabKZmGsuSGNt4xAP8aFISA5c0/t+Ll0+AmFWHNjgysv6YE3FHK28trVHh7VyY27sm2eH0KlQard2Q0KW9/bkIYpGIhguOTzH5dbUjNwnM/n8J14d2w85nxVn+ezuaLw5fg6SJBXLQvSuuv7V8XrmJypA+k4ra/bhQqjUO/Lu3F1j8PichxVVRUwMvLy6z4msUfEwmFQggbfCJ777334t5777V8lUZ4e3sjOjoaGRkZJveTy+XYsmULVqxY0eI5w8PD4evri4yMjGYDlDKZDDKZY/4CR0RERNQe5Eo1JEIhyhQqeLtIoNJq29QjsbPTZ1MZC8Lps6mklhczGeizEJv7g9+Ry0ndpWK8NCkCWp2uSQDv0VG9IBQITD43D5kYUrEQCyZF4JUpUShXqOBV/5p1hCCQXKnGO7szsTIp3XBfS30iGx7bXI9JrU6HR0f1wpyYUMQnpjc5dvb4UBzOLcPQHl6Gicin8yrg6y7Fzf3Ynx8A/jU4CKuT0xv1bZ0TE4rrI3yscn4XichhX5f2xP6cRGSOVv3WWVpaio8//hhnz54FAPTr1w8zZ85sMsTGUlVVVcjMzMSDDz5ocr/vvvsOtbW1eOCBB1o8Z25uLkpKShAYGNimtRERERF1VIpmBpJ09qyftnCXivHy5EijQThrXFdbB0Bt7b292RjW0xu5r8VBrlQ3CuTIlWqznps+yKcPbDjK860rv882uq1uCFBUC8caL93fuCcbi2OjsWhyFAQQNMrSmxMThtnjQzHxvb0Y3tMLG+8YCFeJCC9PicLbt/WHXGndXpjOSB/8vTZwHJ+YDgEEbR5cpeeor0t7cuYPVIio/Vj8E3j37t249dZb4enpiREjRgAA1q9fjxUrVuDXX3/FddddZ/a5XnzxRdxyyy0ICQnBlStXsHTpUohEItx3330AgIceegg9evTA6tWrGx338ccf4/bbb4ePT+NPuqqqqrB8+XLcddddCAgIQGZmJl566SVERkZi2rRplj5VIiIiog6vNVOByTwnrlRgWE9vXHotFtVKjVWzqdylYiycHAkA1wSqQh0+sKzWaPHOrgvIr6zFr4+Owk31w1v0gZzmnpuzBM3bki1m7rHGsvTO5Fehi0yEd27t3+ry8o6s5enxzQeOqW2c/QMVImofFv+2+eyzz2LGjBl4//33IRLV/QOn0WjwzDPP4Nlnn8XJkyfNPldubi7uu+8+lJSUwM/PDzExMdi/fz/8/PwAADk5OY3KyQHg/PnzSE1NRUJC0740IpEIJ06cwGeffYaysjIEBQVh6tSpiI+PZwk3ERERkRH8o912vjiSi/f2ZmPOhFD897aBAKybTdWwnLRYroSXqxgHLpY6fBAqMa0I+ZW18HGTIC7az+g+zlwq25ZsMXOPNZalN7KXN7Y+OgobUrNaVV7e0bHM2H6c/UMHImofFv/rlJGRge+//94QnATqAoPz58/H559/btG5tmzZYnL7zp07m9zXu3dvNDfXx9XVFdu3b7doDURERESdGf9otw2tVocfT+YBAKZF2256sj7YJBMLEbV6B/Ira3HihYkYEOi4gx4/O5wLAPj3sJ4mB2M4a6lsW7LF2ppp1tVV2uwE9M7+gQPLjO3LmT90IKL2YfG/8sOGDTP0nmzo7NmzGDx4sFUWRURERETtQ/9Hu9FtDf5olyvVUKq1KKyqhVKthVypbs9lOp29F68iv7IWXi5iTInytfnj+bhLMTakKwDg44M5Nn+81iqtVuKX0/kAgIdH9LTzamxDny22JC7a8N7ydpVgSVw0Fk6ONJnB6C4VY8GkCCyOjbL4WMC8Dxw6K33w1xh98Jdsy11aN+DKz0MGqVjYabN5icg4i38izJ07F8899xwyMjIwZswYAMD+/fvx7rvvYs2aNThx4oRh30GDBllvpURERERkdSqtFnNiwhr1oNSbPT4UB3KuYkgPb7y1k0N0LPHDibrsyVv7B0Ambp9r9OioXvjpVD6+OJKLNTf1bbfHtcQ3x66gVq3FwMAuGNrDy97LsZmG2WJ5lQr4uktxpVxh1vvl7V0Xmh0g1BJmCTaPZcZERI7N4gClfoDNSy+9ZHSbQCCATqeDQCCARsNpcURERESOzF0qxrzrwqG7Ztr0nJgwzIkJw7HL5XgzJYM97Syg1eoMAco7Bwa22+NO6+2HIE8XXKlQ4JdTBZgxJKjdHttcnx+pK+9+aHgwBAKBnVdjW/r3ReL5Irzy5zlE+Lhh39wJJo+pVKjx1s4MVNVqsPOZsbguvC771tzydg4jMY1lxkREjsvi3yazsow3USciIiIi51OtVCPuf/vwamw08pbGoULxT8aWSCDA+LBumPHFEaPHdvaeds05dKkMueUKeMhEmNrb+BAYWxCLhHhkZDBWJafjk4M5DhegPFdYif0XSyESCnD/sB72Xk67ubV/AJ796SQO5JThSG4Zhvf0bnbf//s7F1W1GvT2c8eEMB+LH4tZgi1z1t6mREQdncUBypCQEFusg4iIiIjs4KdT+TiSW44Xfz2NW/tNbvJHe2FVLYfoWOiH+uE4N/XtDtd2Dgg9OqouQJmYXoSLV6sR0s2tXR/fFP1wnOm9/RDg6WLn1bQf/y4y3D0oCF8dvYz3917ERzO8je6n0+nw/t5sAMBT40JbnWHKLEEiInJG/LiIiIiIqBP77NAlAHUlt0Jh04CIuUN0qI5O9095913tWN6tF+7jjkkRPtDpgM2HL9n88cwdnqTR6vB/9eXdD48Itvm6HM0z40MBAF8fzUVptdLoPqlZV3EqvxJuElGbrxGHkRARkbNhgJKIiIiok8oprUZyRjEA4KFmJipz8q1ljl2pQNbVarhKhLihj79d1vDo6F4AgE8PXoJGq7PZ4yhUGqxNyUTA8gQELEtAwPIEvJmSCYWqaR/65PQiXC5XoKurBLf0726zNTmqsSFdMTjIEzUqbbOBY3325L+H9Wj2QwEiIqKOigFKIiIiok7qiyO50OmA6yN8ENpMKbC+p92SuGhD0MTbVYIlcdFYODmSmVnX+P7EFQDADX384S6zz7W5c2AgvF0lyCmrQXJ6kU0eQ65UY/WODMQnphlaAOiHJ63ZkdEkk/Lz+vLue4f2cMjp4rYmEAjw9LhQAMAHey9Ce03gOL9CYWgN8Ez9fkRERJ0JA5REREREnZBOpzP0BGypnFTf0y5/6VRkvxqLS6/F4l+DA9nT7hqNyrsH2W9AjatEhH8PrRtC88nBtpV5N1fCLREKsSHV+PDM9alZkAj/+TOjUqHC7gslAICHm8nU7Qz+PbQHPF3ESC+WGzKX9T46mAOVRoexIV0xpIeXnVZIRERkP60KUJaVleGjjz7CokWLcPXqVQDA33//jcuXL1t1cURERERkG3uzS5FRLIe7VIS7BrXcK1Hf0+7vy+UIez0Zt358sEkWWGd3Or8SaUVyyMRC3NTXPuXderPqy7x/PpWPYnltq87RXAm3vFaF/ErTw5OqatWG4GZlrQZnX56E7U+Mwchg79Y+JafnIRPjofoPA/Tl3ACg1mixad9FADBkWRIREXU2FgcoT5w4gejoaLzxxht46623UFZWBgD48ccfsWjRImuvj4iIiIhs4LP6Pnh3DwqChwWlyFOjfaHW6pBdWoPt5wtttTyn9H199uTUaD942nl40NAeXhjawxNKjdaQ1WkJUyXcG1Kz4e8hbbZP4qhe3nCVirA2JQMByxPQMz4RwfFJSM0qQa26c/csfXpsCABg6+l8XCqrAQD8drYAueUK+LpL8S8zPiwgIiLqiCwOUM6fPx+PPPII0tPT4eLiYrj/xhtvxO7du626OCIiIiKyvmqlGt8er+uV+PBIy0pu3aRiQ5nu//ZftPranNk/5d2OEWR6cWIkfnpkJB4c3rPFKdvXMlXCvXZnJlSa5ocnbbp7MNbsSEd8Ynqj4GZ8YrrR/pSdSd/uXTApwgdaHQxTzb/6u+6/j47qxbYJRETUaVkcoDx06BCefPLJJvf36NED+fn5VlkUERERkTHN9cMjy/x8Kh8VCjXCurlhQpiPxcc/NTYUAPDbmQLklFZbeXXOKb2oCgIBENBFhlv6OcaU6tsHBuBIbhl6xie1OGX7WmUKlckSbpVGZ3R40pob+6KPnwc2pGYbPfba/pSd0UuT6gLHcyeEIb9SgU/vHYofHxmJ2eND7b00IiIiu7F4tKBMJkNFRUWT+9PS0uDn52eVRRERERFdS98Pb0NqFspqVPB2lWBuTBgWTo5k1pGF9OXdDw7vCaFQYPHxvf09MCnCBymZJfjwQA7ip/ex9hKdilypRk9vV/zy6Ch095BBB/v35pQr1VibkomVSemG+/Ql2gCwYFKEyQns3i4SeLtKjAYpvV0l8JDV9SRdMCkCr0yJQrlCBS8XCdQ6bYvBzXKFCn4esjY+Q+d1XYQPVienY+Y3xww/y2aPD8X03vxbioiIOi+LP7689dZbsWLFCqhUdb90CAQC5OTk4OWXX8Zdd91l9QUSERERmeqH19lLRi11qawGSel1E4QfasNE5afqh3l8fCAHKk3H7itoKnNXHzjvsSIREauS0TM+0ewsRVuyZMq2MSpt8yXcc2PCoNLWfc/1w5P8PGSQioVwk4gNwU1jvF0l8LJzf057kivVWLMjAyuTGpe/r0xi+TsREXVuFgco3377bVRVVcHf3x81NTWYOHEiIiMj0aVLF7z++uu2WCMRERF1cm0NttA/tp0rhI+bFBPDfRDu497q89zWPwDdu8iQX1mLX045d5sfcwKQ106yVqg0Dh04NyeL0RR3qRjzJ4ZjcWxUoxLuJXHRWDg50mT2pbnBzc6IP8uIiIiMs7jE28vLC4mJiUhNTcWJEydQVVWFYcOGITY21hbrIyIiok5ErlRDIhSiTKGCt4sEKq0WLmIRiuVKloy2kf7axkX74d/DeuBKuaJN55OKhZg1qhdWJafjf/sv4l+Dg6y00vZlqnWAQq3Ff3ZnIj6xaZm0i1iI5yeGmww2vTIlqr2eRhMtlWibk8X4wtbTuKlfAC4viUNVrRpe+vdkCy0V3KViLJwcCaDuOrAlwz9Y/k5ERGScxQFKvZiYGMTExFhzLURERNSJGQsUzYkJw3MxYfByFbc52NKZ2ap/5xNjemHNjnQkpxcjragK0X4eVly17en7NMbX92UErglAXhfe7LCXX88W4NFRvRw22KTPYlzR4Lnp6bMYpSaKqcpqVPjscC4+PngJ6QsnIcK37ntr6piGXCSiJv0pzQludnTWCBwTERF1RK0KUCYnJyM5ORmFhYXQXlOi8cknn1hlYURERNR5NBcoik9Mg06nw6zRvTAnJrRRJpueOcGWzsxUEA5oeViKKb26uuHGPt3x29kCfHEk1+mG5Zgqt20pAJleJIeXq+MGm5rLYpwTE2pWYPr3swVQa3Xo6+9hCE62Zg0ADEFavkfbHjgmIiLqqCz+bXT58uVYsWIFRowYgcDAQAgElk9+JCIiImrIVKBo455sLI6NxqLJURBA0KpgS2fWUs+7tpYhv3B9OGaN7oXYaF8UVtbC27UuU661Qc/2ZKrctqUApFqrg1Lj2MGmhlmMRXIlvF3F+Du33Kz3i76v6O0DA2y9zE6F5e9ERETGWfyb4wcffIDNmzfjwQcftMV6iIiIqBMyty+bPthSXK2El4sY+y+W8g/6Fti6593oXl2xKjkdM7855nTBFlPltuYEIEUCOHywSR8oVmm0CHs9GWU1KlxZGgdf9+a/5zUqDf48VwgAuGNAYLusszNh+TsREVFTFgcolUolxo0bZ4u1EBERUSdlbl82fbDFRSxE1OodyK+sxakXr0e/gC7tul5nYsued/ry8ZVJTYfIAG0rH28PKq3WZOsAcwOQzhBsCu3mhl7eriiWK/H98Tw8NS602X2T0oogV2oQ7O2C4T292m+RnQjL34mIiBqz+F/Cxx57DF999ZUt1kJERESdlD5QZIy+VLahbm5SjAz2BgB88XeujVfn3OqubZjRbcaurSVaKh+XCB076OIuFWNuTDgWx0bB27UuUOvtKsGSuGgsnBwJN6nYkO2Wv3QqCpZNRf7SqVgwKaJRANJdKoZULISfhwxSsdBhg7L3DKmbtL7l2GWT+/1UX9592wC2cyIiIqL2YfFvTwqFAps2bUJSUhIGDRoEiaTxp+7vvPOO1RZHREREnUOtSos548Og09X1nDSnVPbB4T3x65kCfHkkF69P7wOhkIEUY9ylYsy7Lhw6nc7sa2suW5eP29qZ/Er86/PDWH1jX+QtnYqKZjIgO0q22z1DgvDy72fxV9ZV5JbVoKe3a5N91Botfj1d33+yP/tPEhERUfuwOEB54sQJDBkyBABw6tSpRtv4CSsRERG1xoLfz2Bfdik23DEAi2OjzSqVvblfd3i5iJFbrsDOzBJMjvJt51U7hxK5ErEf7MPSab2RtzQOFQq11cqQbVk+3h4+O3wJ5wqr8OmhHNw2IMDpA5At6dXVDTFh3ZCadRXfHLuCF66PaLLPnuyrKKlWoZubBNeFd7PDKomIiKgzsjhAmZKSYot1EBERUSe1J+sqPj14CQDgJhEZSmUB04EiF4kIM4YE4cP9OfjiyCUGKJvxycEcHM+rQHziedzWv7tVg3AqrWNPsTZFo9Xhy7/rSp0fGhFs59W0n3uH9EBq1lVsOXbZaIBSX959S7/uEIsc83tHREREHQ9/6yAiIiK7UWu0ePbHkwCAR0f1wthQyzK2HhpeF1j64WQe5LVqq6/P2Wm0Onyw7yIA4JlxYVavdnGXirFwciSWxEU36uG4ODYKCydHOmwvRgBITi/ClQoFurpKcFNff3svp93cPTgQIqEAR3LLkV5U1WibTqfDzyfry7s5vZuIiIjakVm/Nd55553YvHkzPD09ceedd5rc98cff7TKwoiIiKjj27gnGyfyKtDNTYI1N/Wx+PhxoV0R7uOGCyXV+Pl0Pu4f1tMGq3Re288XIutqNbxdJbhvaJBNHkM/ROaVKVEoU6jgLhUh4XwRzhRUYlhPb5s8pjV8frhuuNK9Q3tAJnasidu25OchQ2yUL7afL8LXR69gydRow7ZjVyqQU1YDV4kQcdHMSCYiIqL2Y1YGpZeXl+ETdy8vL5M3IiIiInPkVyiwdPt5AMDqG/vC193yYSoCgQAP1AclvzjMad7Xem9PNgDgkZHBcLNhNqN+irW/hwyv/HkWd312GP/ZfcFmj9dWFQoVfjqVBwB4eETnC2rfO6QHgLpp3jqdznD/Tyfrrsn03v42fb0QERERXcusAOWnn36KLl26GP7f1M0Sy5Ytg0AgaHTr06f57InNmzc32d/FxaXRPjqdDkuWLEFgYCBcXV0RGxuL9PR0i9ZFREREtiFXqqFUa1FYVQtPFwk+u28oZgwOxKxRvVp9zgeG1wWYktKLcKVcYa2lOr0LJXL8eb4QAPD02JB2e9wHh9WV3X93PA/F8tp2e1xLfH8iDzUqLXr7uWNksLe9l9Pu7hgYAJlYiHOFVTiRV2G4/+f6/pO3D+T0biIiImpfdu9B2b9/f+Tl5RluqampJvf39PRstP/FixcbbV+7di3Wr1+PDz74AAcOHIC7uzumTZsGhYJ/sBARkXU0DLIp1VrIlex9aA6FSoO1KZkIWJ6AgGUJ6BmfiL9zy/DxPUMgFLa+N2KkrzvGhXaFVgd8dfSyFVfs3N7fexE6HTCttx+i/Dza7XFHBHtjRE8vKDVaw/AjW7P0Pfn54bp1PTQi2Op9OZ2Bp8s/fTe/PnoFAJBRLMep/EqIhQLc3Le7PZdHREREnZBZtRtDhw41+5e3v//+27IFiMUICDD/U1qBQNDs/jqdDuvWrcPixYtx2223AQA+//xzdO/eHT///DPuvfdeo8fV1taitvafT/grKiqM7kdERKQPsm1IzUJZjQrerhLMjQnDwsmRcJF0nj52lpIr1Vibkon4BtOey2pUWJmUDqFAgAWTIto0UOWBYT2xN7sU/3ckFy8amUzc2dSoNPj0UA4A4Jlxoe3++E+ODcXh745j0/6LeGFiRJsC0C2x9D2ZVVKN3ReuQiD4J/u2M7pnSA/8eDIf3xy7jNU39jFkT14f4YOublI7r46IiIg6G7P+Erj99ttttoD09HQEBQXBxcUFY8eOxerVq9GrV/NlXlVVVQgJCYFWq8WwYcOwatUq9O/fHwCQlZWF/Px8xMbGGvb38vLC6NGjsW/fvmYDlKtXr8by5cut+8SIiKjDaS7ItqL+67YG2ToyiVCIDalZRretT83CK1Oi2nT+GUOC8Pwvp3EirwKn8iswIMCzTedzdluOXsbVahVCurriRjtkw907JAgv/noamSXVSEovwtTetpmS3Zr35BdH6nqVTo70RbC3q03W5Qxu7tcdHjIRLpbW4NClMpzIK4evuxS3DWB5NxEREbU/s/6KWrp0KQBAo9Fgz549GDRoELy9vdv84KNHj8bmzZvRu3dv5OXlYfny5ZgwYQJOnTpl6HnZUO/evfHJJ59g0KBBKC8vx1tvvYVx48bh9OnT6NmzJ/Lz6z757d698S/i3bt3N2wzZtGiRZg/f77h64qKCgQHB7f5+RERUcdi6yBbR1amUKGsRmV8W40K5QoV/DwsH5Kj181NiqfGhmBSpC8ifNxRWFULbxcJVFptpwsa63Q6vLc3GwDw1NhQiGyYvdgcd5kYD44IxsbULPxv30WbBSgtfU/qdDp8cURf3t15sycBwFUiwjNjQzE2tBsGBHhi+bQ+eP+uQahVa+29NCIiIuqELOpBKRKJMHXqVJSWllrlwW+44QbcfffdGDRoEKZNm4Y//vgDZWVl+Pbbb43uP3bsWDz00EMYMmQIJk6ciB9//BF+fn743//+16Z1yGQyeHp6NroRERFdy5wgW0dgix6b3i4SeLtKjG9zlcDLxfg2S6y8oQ+O5Jahx4pEBCxLQMDyBLyZkgmFStPmczuTk3kVuFhaA5lYiFmj7feB61Nj6gbzbD1TgMvlNTZ5DEvfk3uzS5FZUg13qQh3Dgi0yZqcyeK4aBzJLUPP+ERErEpGcHwS1v+V1eneM0RERGR/Fg/JGTBgAC5cuGCLtcDb2xvR0dHIyMgwa3+JRIKhQ4ca9tf3piwoKGi0X0FBgUV9LomIiIxpjyCbvV07yObaIF9rg5dylRqzx4ca3TY3JgwqbduytuRKNd7cmYmVSemGgJW+1HfNjgynGGTU1sCw/viublJkvToFO58ZB1/31meltlW/gC6YGO4DjVaHD/fn2OQxLH1PJqUVwdddin8NCoS7rHNl1l6rI7xniIiIqOOwOEC5cuVKvPjii/jtt9+Ql5eHioqKRre2qKqqQmZmJgIDzftEW6PR4OTJk4b9w8LCEBAQgOTkZMM+FRUVOHDgAMaOHdumtRERUeem0+lwtrDSpkE2e5Mr1Vi9IwPxiWlNAhYfHchBTQvBy+YoVBrM+fEk5sSEYXFslCGg5O0qwZK4aCycHNnmMuyWSn0lQot/5WlXLQWGLTk+ZGUSguOT8MfZQrtnwj05ti6L8qMDOVBrrPv+0Gh1+PtyebPvydnjQ/FXVglySqsNwduHRgYj69UpWDG9j1XX4oyc/T1DREREHYvFfw3ceOONAIBbb7210WRvnU4HgUAAjcb8X4RffPFF3HLLLQgJCcGVK1ewdOlSiEQi3HfffQCAhx56CD169MDq1asBACtWrMCYMWMQGRmJsrIyvPnmm7h48SIee+wxAHUTvp9//nmsXLkSUVFRCAsLw2uvvYagoCCbDvohIqKOTafTYcn28/jhRB52PTMOAgGwITXbMDF49vjQDjHF21TAIszHDWvqg5d65gwI0ul0eOzb4/jq6BVklFRj66OjsDg2GuUKFbzqe0Ra47rZuselLbV1+FJzx8cnpkFgxvG2dOfAQPi5S3GlQoFfzxTgjoHWKavW6XR47udT2JFRXP+eFDSa4j0nJgxzYkLxyNfH8Om9Q7A2JaPRe9bUlO/OwpnfM0RERNTxWPzbakpKitUePDc3F/fddx9KSkrg5+eHmJgY7N+/H35+fgCAnJwcCBt8eltaWorHH38c+fn56Nq1K4YPH469e/eiX79+hn1eeuklyOVyPPHEEygrK0NMTAy2bdsGFxcXq62biIg6NrlSDYlQiDKFCt4uEpwrqsIPJ/JwrrAKf54rxEuTIvHqlGhcrVGhi0yE5PRi6HQ6ey+7zZoLWPi6S3F9hA8e/Oqo0eNMDQiKT0zHV0cvQywU4PUb+sC/PuChD3xILS/mMEpf6mts/Y5eft/W4UuOPLxJKhbi0dG98MaODGw5etlqAcr4xHS8tzcbAgFw6FIZXpoUgVenRDUKfFcq1FgwKQIbUrOwMindcKwlwd+OzJnfM0RERNTxWPwb2cSJE6324Fu2bDG5fefOnY2+/s9//oP//Oc/Jo8RCARYsWIFVqxY0dblERFRJ6QvlW2YjTV7fCh2PTMOf5wtwEMj/hk60t1DihHrduPo5Qp8++Bw/GtwkB1X3nbNBSwCushQVKU0mW1VXK2Er5sEKq3OENz1lIkxKMgTffw9MO+6cEyJ8rPZ2lVaLebGhBkCTw3py++tFQy1trZmsjl6JtzscaEY06srYqN9UVCpQFdXqcXT1Rt+aNDwdfXMuFDc2Le7Yb+GgW93qRjd3KS4Y/Nho+e0d/DW3pz5PUNEREQdj1m/GZ44cQIDBgyAUCjEiRMnTO47aNAgqyyMiIiovTVXKrsyKR0CAfDSpMhG+wsEAsRF++Po5Qp8c+yK0wcoVVot5sSENXr+AJBfWYvuXWQms628XcSorNVgfeqFJuXv++bEwKuZQSbW4i4VY+Hkuu/P+kalvo5fft9SJpuHTAx5rQoQCBpl9qq0WqQVytHb392hM+F83KU4kluGmd8ca1WJdXMfGpjzunL04K09NfeeYfk7ERER2YNZAcohQ4YgPz8f/v7+GDJkCAQCgdFSNkt7UBIRETkSU6WyG1Kz8eqU6Cb33zskCGtTMvD72QJUKFTwdOKySHepGPMnhkOn02Hjnn+CjM+MC4Va03y21ZyYUBTLlfjoQE6TUtqVSekQCgTtUkrrIhFhwaQIvDIlCkVyJbxdxTiQU+rwgRZTmWyzx4diT/ZVDAnywobUzCbB3zkxYdibXYrZ40MbXXs9e2fC6YP+rS2xNvWhgTmvK5Yxm9bwPWPtvrBEREREljDrL4WsrCxDX8isLON/uBERETm71mRbDQ7yRG8/d5wvkmPr6QI8MLxneyzVZmb/eBJ3DQrClSVxqKxVGwIWLWVbCQUCbNyTbfSc7VlKqw9WuYiF6L0mBVcqFNg3JwajQ7q2y+O3hv7aaq8JDM+NCcOC6yPw9+VyrP/rgtHgLwDMGByERVOiIBQIHC4Tzt79NVnG3DL9e8bafWGJiIiILGFWgDIkJMTo/xMREXUkrcm2EggEuGdID6xITMM3xy47dYCyQqHCN8eu4Mu/LyPrlSkI6eYG4J+Ahalsq8KqWocqpfVxlyIu2hefHc7F+tQsfOnAAUoA0Op0GN7TG5dei4W8VgNv138Cw6N7dcVtnx4yetzGPdlYHBsNqVjokJlw9u6vyTJmIiIiIufQ6lqrM2fOICcnB0qlstH9t956a5sXRUREZA95FYpWlcreMyQIKxLTsP18Ea5WK9HNTdoey7W6nZklUGt1iPR1NwQnr9VctpUjltLOiQnDZ4dz8d3xK1h7c1/08HJt9zWY62BOGe7YfAiDArvg2AvXA/jn2pobpHPETLi2vi6s8bpiGTMRERGR47P4N9cLFy5g8ODBGDBgAG666SbcfvvtuP3223HHHXfgjjvusMUaiYiIbC4loxi3fXoIc2LCsDg2Ct71wze8XSVYEheNhZMjm+1117d7FwwO8oRaq8OPJ/Pac9lWlZhWBACIjfK1+Fh9Ka0x+uBuexvW0xsxYd2g1urw/t6L7f74lth7sRQA0Me/S5Nt+iCdMY7eR7Gtrwv94KbWHq/nLhVDKhbCz0MGqVho836oRERERGQZiwOUzz33HMLCwlBYWAg3NzecPn0au3fvxogRI7Bz504bLJGIiMj65Eo1lGotCqtqUavWoEqphlKjxYrENLw0KRL5S6eiYNlU5C+digWTIlrMtrpnSN0E72+OXWmP5dtEUn2AcmpvP4uP1ZfSLomLtii4a2v64Nim/RehUDnuIL992VcBAGOMlKI7YvDXXM29LhbHRuFlM14X7lIxnr8u3OIPDYiIiIjIuQh0xsZxm+Dr64sdO3Zg0KBB8PLywsGDB9G7d2/s2LEDL7zwAo4ePWqrtbabiooKeHl5oby8HJ6envZeDhERWZlCpcHqHRnY0KAn3ezxoXj+unB4SEWQii0v/bxQIkfk6h0QCoDc1+IQ4Olig5XbTk5pNUJfT4ZIKEDx8mnwaiZjryVypRoSobBRKa09g0hqjRaRq3cgp6wGH80YjEdH9bLbWpqj0+ngt3Q7rlarsH9uDEb1ahqkVKg0WLMjw2n7KDZ8XbhLRdh+vghlNSrMbOH7UVRVi9j/7cfyab1xQx8/VCjUDvG6IiIiIqKWWRJfsziDUqPRoEuXuvIjX19fXLlSlykSEhKC8+fPt2K5RERE7UeuVGP1jgzEJ6YZ+trpJyKv/ysLKq1Fn9sZhPu4Y1SwN7Q64PsTzlfmnZhWDAAYFezd6uAk4HiltGKREM+MDwUArP8rCxZ+Ltsu0orkuFqtgotYiCFBXkb30fdRtDSz11E0fF38eDIPd312GKuS06Ft4f328cEcnMyrwKqkNMjEIod5XRERERGRdVkcoBwwYACOHz8OABg9ejTWrl2LPXv2YMWKFQgPD7f6AomIiKxJIhRiQ2qW0W3rU7MgEbZ+sMg9Q/Vl3pdbfQ570fefjIu2vLzb0T02uhdcJUKcyKvArswSey+nib315d0jg70hFTf/+nO04G9r3TkwEN6uEmSWVGP7+cJm99Nodfjfvrreoc+MN17iTkREREQdg8V/hS1evBja+l5HK1asQFZWFiZMmIA//vgD69evt/oCiYiIrMmcicitNWNwEAQCYE92KS6V1bT6PO1Nq9UhOb3jBii7uUnx4PBgAHVBaEejH5BjrP9kR+QmFeORkXXfj/f2Zje73+9nC3CxtAbd3CSGHq9ERERE1DFZHKCcNm0a7rzzTgBAZGQkzp07h+LiYhQWFmLy5MlWXyAREZE12XIicg8vV0wI6wYA+NaJhuUcvVKOkmoVPF3EGNXL297LsQn9kJlfTufj4tVqO6+mMf2AnHGh3ey8kvbz9NgQAMAf5wqRVWL8+/HenmwAwKOjesHVSUrZiYiIiKh1Wl/H1kC3bt0gEAiscSoiIiKbsvVE5HuG9AAAk6WrjibhfF325KQIH0hEVvnVwOH0C+iCR0cF48eHR8K/iwyFVbVQqrWQK9V2XVdZjQpnCqoAAGM7SQYlAET5eWBabz/odMD7+7KbbE8rqkJCWhEEAuDpsaHtvj4iIiIial8d868QIiKiZrhLxXhhYgQWx0YZMim9XSVYEheNhZMj29zXb8bgIPw8cyR+mjkSBZUKhwiCtSSpvv9kbAcs725o3W0DcCS3DD1WJCJgWQIClifgzZRMKFQau61pf315d6SvO/y7yOy2Dnt4ZlwoAOCTgzmoueZ78EF978kb+/gjzMetvZdGRERERO3MOburExERtcGS7ecwMcIXl5fEoapWDS8XCVRarVUmIrtLRTh8qQyPbDmGshoVvF0lmBsThoWTIx1y4rK8Vo3U+hLjqR04QClXqvHmzkysTEo33FdWo8KKxDQAwIJJEXYZOqMfkDOuE2VP6t3YtztCurriYmkNvjl2xdCXslqpxuZDlwD8E8QkIiIioo6NGZRERNSpqDRafHroEu7cfAhn8iutOhFZrlRj9Y4MrExKNwzi0QfB1uzIcMhMyt0XSqDS6BDS1RWRvu72Xo7N2HJ6e1vs62QDchoSCQV4sr4X5Xt7/vnefHX0MspqVAj3ccO03v72Wh4RERERtSOzfhsfNmwYSkvrfoFesWIFqqsdq7k8ERGRufZll6JCoYavuxRDenhZ9dyOGgQzJTG9GEDd9O6O3E/altPbW0uj1eFATt3vV51pQE5Ds0b1glQkxOHcchzMKYVOpzMMx3lqbCiEwo77miQiIiKif5j1l9LZs2chl8sBAMuXL0dVVZVNF0VERGQrf5yrG14zrbcfRFYOfjhiEKwlifUDcuI6cHk3YNvp7XpypRpKtdbsATyn8itQVatBF5kY/QO6tPnxnZGfhwz3DAkCAHxxOBfHr1Qgt1wBF7EQj44KtvPqiIiIiKi9mFXPNmTIEMycORMxMTHQ6XR466234OHhYXTfJUuWWHWBRERE1vTnuQIAwA19rF86qg+CGQtSWisIZk1XyhU4XVAJgQCYEuVr7+XYlH56u77nZEP66e3SNnS+Uag0WJuSiQ2pWWb3Ht2brS/v9rZ6sNyZzL8uHHcODERstC9K5CpkvToFp/Mr0c1Nau+lEREREVE7MStAuXnzZixduhS//fYbBAIB/vzzT4jFTQ8VCAQMUBIRkcPKLavByby6gJwtetvZOghmbYn107tH9PTu8MEgd6kYCydHAqgrt2/NACO5Ug2JUIgyhQre9YOV3KViyJVqrE3JRHyD77s5A3j21Q/IGRPSOcu79Xr7e+CHk3mY+c0/g6XmxIRhUKCnQw6WIiIiIiLrMytA2bt3b2zZsgUAIBQKkZycDH9/Ni0nIiLn8md9effoXl3h4279gFxzQbDZ40PxwkT7TIk2JSm9LkAZG92xsyf1XCQiLJgUgVemRCG/shY+7hLklinMCoKZypAUt9B79JUpUUa37b2o7z/Z+Qbk6OmDu9dOV49PTIMA9puuTkRERETty+I0Dq1Wy+AkEVE7aamnnaU97zq7bfUBSluUd+vpg2D5S6eiYNlUXF4Sh6E9vHDTxwdQVes43x+tVmfIoJzawftPNuQuFUMqFmLbuQKEvZ6M+VtPt3iMfjp7fGJak+nsHx3IwdVqpcW9Rwsqa3GhpBoCQV3AvLNyxsFSRERERGR9rfpIOjMzE+vWrcPZs2cBAP369cNzzz2HiIgIqy6OiKgza6mnXWt63nVmSrUWifUZgzf2te0HbfqMLz8PGWrVGiz47QwulFTjzZ2ZWD6tt00f21xnCivh7yGDq0SEsZ2wxHhCuA+K5UokpxejQqGCp4n+oKaCaP/96wKeGBNice/RfRfryrv7d+/S7PCezsCcwVJ+HrJ2XhURERERtTeLP5bevn07+vXrh4MHD2LQoEEYNGgQDhw4gP79+yMxMdEWayQi6nRMZWyt3pGO4qparN6RbnT7mh0ZzKQ0IjXrKqpqNfD3kGJokFe7Pa5MLMIbN/UFALy1MwOXy2ts/pjmZN5G+Ljjl0dH4fSCSVBptTZfk6Pp270Levu5Q6nR4o+zhSb3NRVEyyyphlylxtyYMKPb59T3Hr3WPwNyOm/2JNA+09WJiIiIyPFZHKBcuHAh5s2bhwMHDuCdd97BO++8gwMHDuD555/Hyy+/bIs1EhF1OqYytr76+zI8XSTYkJptdDvLIo37s0F5t7CdJybfOTAQMWHdUKPS4rU/z9v0sfSZtQHLExCwLAEByxPwZkomFCpNo+09ViQiYlUyesYnNtremdw+MBAA8NPJPJP7tRREc5fU9R5dEhdt2M/bVYLFsVGYGxMGiZHXm35AzrjQzpe92pB+sJQxc5sJ7hIRERFRx2PxX7Bnz57FrFmzmtz/6KOP4syZM1ZZFBFRZ2cqY8tVIkJpTctlkc7O2v01/zxXAAC4oU93ayzPIgKBAG/d0g8A8NmRSzh6ubxN52vu2pjKvF2zIwMVCpXJ7Z0t8/aOAQEAgD/PF5oM0JoTRLu292j+0qkYE9IVE97dgzdSMhsdo1RrcTi37jXQmQfkAP8Mlro2uLskLhoLJ0dyQA4RERFRJ2FxgNLPzw/Hjh1rcv+xY8c4PIeIyEpMZWzVqDTo6tqxyyJbygK01MWr1ThTUAWhAIiz08TqUb264r6hPaDTAS9uPQ2dTteq8zR3baqVGoiEgmYzb7/8OxcuYhEHkjQwoqc3eni5oKpWg6T04mb3c5eKsWBSBBbHRpkMoukH8Ph5yCAVC1FZq8a5wiqsSk5HRrHccL6jl8tRq9bCx02CKF932z5JJ2AsuLtgUgR76RIRERF1IhZ/LP3444/jiSeewIULFzBu3DgAwJ49e/DGG29g/vz5Vl8gEVFnpNJqMScmFPGJ6U223T+sJxRqDebGhGFFYlqT7fqMLqnln0E5BLlSjbUpmYhv8Nz0WX4AsGBShMVZVfry7nGh3dDVTWq9xVpo1Q19cDq/EnMnhKNWrUVFrRreLhKotFqznpOpa+PrLsVdgwLbnHnbmQaSCIUC3NY/AO/tzcZPp/Jwc7/ms2uXbDuPCeE+uPxaHKqUanjVf99MBdFmDA7CpwcvISGtCLN/PIk/Hx8NgUCAvfUDcsaGdINA0L7tBhxVw8FSAJz25xcRERERtY7Fv/299tprWLJkCTZs2ICJEydi4sSJ2LhxI5YtW4bFixdbdK5ly5ZBIBA0uvXp06fZ/T/88ENMmDABXbt2RdeuXREbG4uDBw822ueRRx5pcs7p06db+jSJiOxLB8weH9Zsxpani6TZnnfzJ4Y7dVmkqf6brc3y0wcop/exb6Z/SDc37HpmHI7kliFoRaLF2aEtTZP2cZN26szb1rhjYF2Z96+nC6DWGO93mFVSjf/+dQF3bj6EzKtyQ4ZkS+8zgUCAjXcOhEwsREJaEb47Xtfrcl/9gJyxnby8m4iIiIhIz+K/8gQCAebNm4fc3FyUl5ejvLwcubm5eO6551qVBdC/f3/k5eUZbqmpqc3uu3PnTtx3331ISUnBvn37EBwcjKlTp+Ly5cuN9ps+fXqjc3799dcWr4uIyJ4+OpiDie/txYSwbs2WPV5bFnllSRyG9vDC9e/tRWaDclJnY6r/Zmv6aypUGiTXl+/eaOcApVypxju7L2BlUnqrekC2ZZp0w8xbYzrrQJLrwn3Q1VWCYrkSe+oH11zrP39dgFYHTI32w4AAT4vOH+nrjkWTowAA87aeQkWNClerVfB1l3b6/pNERERERHptSrHp0qVL2xcgFiMgIMCsfb/88stGX3/00Uf44YcfkJycjIceeshwv0wmM/ucAFBbW4va2lrD1xUVFWYfS0RkbWqNFut2X8DF0hpcuFqNuN7CZsseG5ZF6nQ6vLsnG8euVOCpH04g4YkxTlk+qu+/aSwQ15osv7+yrqJapUGgpwyDgywLLllbS9mhr0yJMnl8S9dGP01af76yGhW8XSWYGxOGhZMj4SIRtbi9s5GIhLilX3d8fiQXP53Kx8SIxj1Kr1Yr8cmBHADAi9dHtOoxXp4cgf05V/HkmFCIRUJ8OGMw/D2k0Ghb14eUiIiIiKijsXuDn/T0dAQFBSE8PBz3338/cnJyzD62uroaKpUK3bp1a3T/zp074e/vj969e+Ppp59GSUmJyfOsXr0aXl5ehltwcHCrngsRkTX8eDIfF0tr4OsuxUMjzP95JBAI8MG/BsFFLERyejE2H7pkw1XajlKjxezxoUa3tSbL74+zddO7p/fxt3vAtq3ZofrepMaYmiZtKvOWA0mAOwYGAgB+PpnfZHjR+3uzUa3SYEiQJ6ZEtW7AkkwswrcPjsCR3DL0jE9ExKpkBMcn4Z1dF1o9+ImIiIiIqCOxa4By9OjR2Lx5M7Zt24b3338fWVlZmDBhAiorK806/uWXX0ZQUBBiY2MN902fPh2ff/45kpOT8cYbb2DXrl244YYboNE0/wfAokWLDOXq5eXluHTJOf+oJyLnp9Pp8PauTADAs+ND4Wph0CjS1x3Lp/UGALz46xnkVyisvkZb++jARcyJadp/c3FsFJ67LhxuFl4Tff9Je5d3A6ans5uTHeomEWFuTLjF06Sv7ZXY0vbOZmpvP7hJRMgpq8HRy+WG+xUqDTbuyQYAvHB9RKsD3HKlGm/uzGx1aT8RERERUUdn179IbrjhBsP/Dxo0CKNHj0ZISAi+/fZbzJo1y+Sxa9aswZYtW7Bz5064uLgY7r/33nsN/z9w4EAMGjQIERER2LlzJ6ZMmWL0XDKZDDJZ55laSkSOKzXrKg5dKoOLWIhnxoW26hzzrgvHN8euoFqlQXZpNbq5SVGmUFk0LdpeUjKK8cKvZ7Bpfw5+mzUKi2OjUa5QwdNFjD/PFWL8hlQ8My4Us5vpo3itrBI5pCIhArrIEBvlZ+PVt0yl1bZp+npKRglm/3QSb9zcF/mxU1GuUJk1TZpMc5WIML2PH348mY+fTuVjWE9vAMAXR3JRUFmLYG8XzBgc1Orzt7W0n4iIiIioo7Mog1KlUmHKlClIT0+3yWK8vb0RHR2NjIwMk/u99dZbWLNmDRISEjBo0CCT+4aHh8PX17fFcxIROQJ99uRDI4INfSctJRYJ8fm/h2LXM+Pwx9lCBCxPsHhatD2UyJV46Ouj0OmA8WHdEO7jbsjyk4lFuFBSjXOFVZi/9TT2ZBkfZtKQXKlGoKcLfnl0FDIWTYFYZP9+nO5ScbPT119ukAHZnPf2ZuFcYRW2nStiBqSV3T7gnzJvANBqdXin/v34/HXhkIhaX3Ri7cFPREREREQdjUW/bUskEpw4ccJWa0FVVRUyMzMRGBjY7D5r165FfHw8tm3bhhEjRrR4ztzcXJSUlJg8JxGRIzhfWIWtp+v6Jc67LrxN5wrp6ooNe7KcpqRUp9PhsW+P4XK5Ar393LHu1v5N9pl3XTjuGRIEtVaHGV8cNlm+rlBpsDYlE0Er6vr99YxPdJjg7LU9IC+9FouhPbzw+5l8k8flltXgl/rXR2uza6l5N/X1h1gowOmCSmQWV2FnZglKqlXwchHjsVEhbTp3W0v7iYiIiIg6OovTAR544AF8/PHHVnnwF198Ebt27UJ2djb27t2LO+64AyKRCPfddx8A4KGHHsKiRYsM+7/xxht47bXX8MknnyA0NBT5+fnIz89HVVUVgLoA54IFC7B//35kZ2cjOTkZt912GyIjIzFt2jSrrJmIyFb+s/sCAODW/t3R29+jTeeSCIXYmJptdNv61CxIhHafkQa5Ug2lWovCqlrUqrV4eGQvDAz0xFcPDIe7rGlGoEAgwId3D0b/7l3g5SJBWlGV4XilWmsIusqVaqzekYH4xDSHDc427AH5+eFLuOuzw1j0xzmoNc0PAPrf/ovQaHW4PsIH/QO6tONqO4eublI8NKInfnpkJIK8XBHh64asV6cg5elx6OLStgxVfWm/Ma0Z/ERERERE1NFY/Bu3Wq3GJ598gqSkJAwfPhzu7u6Ntr/zzjtmnys3Nxf33XcfSkpK4Ofnh5iYGOzfvx9+fnV9wnJyciBs8Ef0+++/D6VSiX/961+NzrN06VIsW7YMIpEIJ06cwGeffYaysjIEBQVh6tSpiI+PZ49JInJoV6uVhmnT86+LaPP5zCkpbW0JuTXoMxw3pGahrEYFb1cJZo8PxV/PjoOniWwyD5kYv84aCXepGBtSs3D75sOG4+fGhOHF6yMgEgqcqt/fQ8ODsWTbeWSWVOPb41fw72E9m+xTq9bgw/0XATB70pbeubU/3tqZiZnfHGv0uurj79GmHp/60n6g7jXY8NwLJ0eyfygRERERdXoCnU6ns+SASZMmNX8ygQA7duxo86LsraKiAl5eXigvL4enp6e9l0NEdiRXqiERCm02ZEZ//iJ5LbxdJTiYU4brI3xaPS1YT6nWImB5gtEgpberBPlLp0Iqtk8WpVypxtqUTMQbGRSzJC4aCyZFmLzGdcdnID6xaT/k9bcPwF2DAtFjRWKzxxcsm2rX4KwxK5PSsGTbefTv3gXHX5gIobDx9/+rv3PxwFdHEeTpgqxXp7SpHyIZ19bXpbmPIREKGw03Yv9QIiIiIuqoLImvWfxbcUpKSqsXRkTkTIxl+Vkz48nY+efEhGJsSNc2n7+t06Jtqa0TjeuOzza67b9/XcATY0Lg7SppNjjriP3+Zo8Pw1s7M3G6oBJbz+QbBrbovbc3GwDwxJgQBidtpD0mbeuDkfoAub3eg0REREREjqbVvxlnZGRg+/btqKmpAVA34ICIqKOwdR/D5s4fn5hulfM3Ny16SVw0FpoxLdqW2jrR2NTxmSXVkKvUTtfvz9tVYijdXpWU3ujf1GOXy7E3uxRioQCPj+llpxV2fJy0TURERERkPxYHKEtKSjBlyhRER0fjxhtvRF5eHgBg1qxZeOGFF6y+QCIie2gpm6qtQ2ZsfX6g8bToi4tjcem1WNzSv7vd+921daJxS8e7Sxw3OGvKvOvC4SoR4nBuORLSigz3v1ufPXnXoEAEerrYaXUdHydtExERERHZj8V/Ac+bNw8SiQQ5OTlwc3Mz3H/PPfdg27ZtVl0cEZG92Dqbqr2ytfTToi+WViPs9WRM/d9+KFQaq5y7tdKL5Zg9PtToNnMyHM2ZiNwwOFuwbCryl07FgkkRdg/OmuLnIcPjY0IA1GVRAkBptRJf/Z0LgMNxbI2TtomIiIiI7MfiNJKEhARs374dPXs2njIaFRWFixcvWm1hRET2pM+mskUfw8vlNejqKm3XPoljQ7rBTSJCTlkNfjiZh/uNTIpuD+/sysRHB3Kw65lxEAiADanZFvf3NHcisjP2+3txYgTe35uNv7KuYn/2VaQVy+EuFSPSV4aYsG72Xl6HxknbRERERET2Y3GAUi6XN8qc1Lt69SpkMseaikpE1FrWGjJz7RTwi6XVuP+rv/HqlGjMHh+KlUlNJ1HbYoiNSCjAo6N6YVnCeXy0P6ddApTXPvfMEjk+OpCDc4VV+Pb4Fbw0KRKvToluNNHY3CCQPkPylSlRrTreUfX0dsXLkyIxvKc3BgV5IaB+andumaLNk92pZR31dUVERERE5OgsDlBOmDABn3/+OeLj4wEAAoEAWq0Wa9euxaRJk6y+QCKyv2sDTSqttlEfv5a2OyN9NpVWp8PGPf9k+c0eH4r5E8PNen7GpnTPHh+K32eNxpodGYif3htCgaDdsrUeHRWMFYnnsetCCdKKqhDt52H1x9Br7rnvemYcvvr7Mp4ZF2oIuLU2w9EZMyTNsXByFNbsSMfMb44xi88OOurrioiIiIjIkQl0Fo7fPnXqFKZMmYJhw4Zhx44duPXWW3H69GlcvXoVe/bsQUREhK3W2m4qKirg5eWF8vJyeHp62ns5RHalUGmwekdGo0BTw2BJS9udmVypRnJaMaZE+6KqVgMvFzH+PFeI15PT8NujoxFgYmCJXKnG2pRMxBvJwHwtLgoLro+Eh0xsCO42zNayZXD31o8P4rezBXhhYgTevKWfTR6jpef+0iTHHVRjb6au3ZK4aCyYFMFrR0RERERETsGS+JrFaQEDBgxAWloaYmJicNttt0Eul+POO+/E0aNHO0Rwkoj+IVeqsXpHBuIT0wy9EstqVFiRmIY1OzJQoVCZ3C5Xqu25/DZLTCvC7ZsPYfJ7e+HvIYVUJMQbOzLwd24FFm87b/JYU1O6N6RmQyqq+/GrH2Lj5yGDVCy0efDpsTG9AACfH74Epdo2Qz9aeu7WmFDeUbXHdHciIiIiIiJH06q/dLy8vPDqq6/i22+/xR9//IGVK1ciMDDQ2msjIjszFSz58u9cuIhFHTqY8vvZQgDAmNBuEAgEEAoF+M9t/QEAnx7KwdHL5c0e215Tui11Yx9/BHm6oEiuxC+n823yGI763J0Brx0REREREXVGrYoelJaW4q233sKsWbMwa9YsvP3227h69aq110ZEdmYqWOIqEaG0puMGU3Q6Hf44WwAAuKmvv+H+saHd8O+hPaDTAfN+OYXmumR4uYjh7Wp8ErctpnSbSywS4pGRwQCAjw5cbNO55Eo1lGotCqtqoVRrIVeqUSJXoovMMZ+7M9BPjze6jdeOiIiIiIg6KIsDlLt370ZoaCjWr1+P0tJSlJaWYv369QgLC8Pu3bttsUYishNTwZIalQZdXTtuMOXo5XLkVdTCXSrCxAifRttW39QXrhIhdl+4ih9O5DU59sSVCiSlF2P2+FCj59ZP6baXWaPqyrwT04qRVVLdqnPoh+AELE9AwLIEBCxPwNqUDGh1OuzJuuqwz93R6afHG8NrR0REREREHZXFAcpnn30W99xzD7KysvDjjz/ixx9/xIULF3Dvvffi2WeftcUaichOTAVL7h/WEwq1psMGU/Tl3XHRfpCJGw/7CfZ2xYvXRwIAXvrtDBQqjWFbelEVpn24Hwt+PYPnrwvHkrhoQxDX21WCJXHRWDjZvkNiwnzcMDXaDwDw0UHLsyib600an5iODalZ8JCK8fLkSId87o5OPz2e146IiIiIiDoTi6d4u7q64tixY+jdu3ej+8+fP48hQ4agpqbGqgu0B07xJvpHjUqD1cnp2Lgnu9kp3mt2ZGB9gynes8eHYv7EiGazK53BmP/+hYOXyrDp7kF4bHRIk+3yWjVu/uQgnp8Qjqm9/VBZq4aXiwQpGcWYv/U0XMRC7HxmHERCQbtO6TbX98evYMYXRxDoKcPFV2MhFpn/eZVSrUXA8gSj5f3erhLkL50KqVjY7hPKOxJeOyIiIiIicnaWxNcs/mtn2LBhOHv2bJMA5dmzZzF48GBLT0dEDu6nk3kY1tMbua/FQq7UGIIlLpK6rEIXiQgLJkXglSlRKFeo4Okixh9nCzFuQyrevLkfburX3c7PwHKFlbU4lFsGALixj/H1u8vE2ProKLyZkoGZ3xxrFJz969nxAADPBiXufh4yAIC0da1/re7W/gEYF9IVCyZFQq3V4WpNLbzNDISZM8jl/9u77/CoyrQN4PdMpqWQhJAOgSSEANJRwNAhIaG4wlpWAeuiWABhXVGiCAjsorg2EPVTkaIrRUVsgIRQJBKqCQRESJWSBoG0gWTa+f4IM8uQmcxMpqXcv+uaSznnzDnvHB/nOI/v+zxBPnLDeZraZ28OeO+IiIiIiKg1sSpBeeLECcPfP/fcc5g9ezZycnJw5513AgAOHjyIVatW4fXXX3fOKInIbd7el4ffLlbgo/t6YfqdkQDqJ0tuTabsz7+CP0qr8cRXx5H1wggEestdOmZ7bf+jFIIA9G/vh3A/hcljlCoN/rM3F0t3ZRu2lV9XY+mubIhFIswd1dlVw20UmUSMn54YhLf25RolWG+eHWuOvjapuRmUzbn2KBEREREREbmeVQnKvn37QiQSGXWrffHFF+sdN2XKFDzwwAOOGx0RudXxwgr8drECUg8R7ukVZvX7/jWuG37+oxSnS6vx7DdZ2PTw7RCJRE4cqWP9dKN79/ibunffSioWY2Vavsl9K9Ly8XJ8F6eMzVGUKg3e/iWvXoJ1ccpZAMDcUZ3NzqTU1ybVH3szfe1RzvgjIiIiIiIia1mVoMzPN/0jnIhatjVHzgMAJvYItWkWpKfUA+un9EPcijR8faIIGzIuYkr/Ds4apkOptTrsPHsJAHBXA8vTrV3m3FTZk2D1lknw4qjO0AmC2dqkRERERERERNayKkHZqVP9BhFE1LLVarT477ELAIDHB0TY/P7bO/hjfkIsNmZehJ+nFLUaHSpq1FbXOXSXtPwrqKzRINhHhjs6+Js9rrkvc7Y3wfre/vwbtUnHQKnS1KtNSkRERERERGStRmUICgsLkZaWhtLSUuh0OqN9zz33nEMGRkTu9cOpEpRdUyPcV4HEruaXOjckOT4GM4ZG4r1f8vDwlxnNYqbdj7/XLe8e1y0YYrH5ZenNfZmzPQnWGrUWb+3LxZVrauycPggJsXXx0ZQ/LxERERERETVdNico165di6eeegoymQzt2rUzqisnEomYoCRqIdbeWN79yB0d4NFAoq4hKq0OK/Y3rs6hu2y7UX9yQveGu497yySYNzoGQN2S6OaQfL2ZPQnWrSeLceWaGh38FBgVE+TsoRIREREREVELZ3Nm4NVXX8WCBQuQnJwMsZizZYhaoosV17HjTCmAxi3v1qurc1hgcl9TbCSTe1mJM5eUkIhFGBNrOfGmkHpg7qjOeDm+Cypq1M1qmbO5BOusoZEWE6yfHjoHAHh8YMdGJ6+JiIiIiIiI9GxOUF67dg0PPvggk5NELdj6oxegE4BhUQHoEuTT6PM0t0Yy+u7dw6IC4OdpXQ1J/QxQ/edoTsucb06wXr6mgp9Cgn25ZWgo55h7WYndOZchEgF/tyN5TURERERERKRn8y/padOm4auvvnLGWIioCRAEwdC9+/GBHe06l77Oocl9TbCRjD5BOaGB7t0tjbdMAplEjEAvKQavTMNdqw8b/vmbsvpw3ezJxNggdArwctUwiYiIiIiIqAWzeQblsmXLcNddd2HHjh3o1asXpFLjBMPbb7/tsMERkev9WnAFOZeV8JZ54L7eYXadqzk1kqmu1WBf7hUAlutPtkQyiQemDeyIOd+dwhu7c/D3gR0h9TD+Z6PR6gy1SZ8YZF/ymoiIiIiIiEivUQnKn3/+GV27dgWAek1yiKh5++xwXQLqb33D4SO3r4GN+TqHTa+RzOFzV+GrkMBPIUFskLe7h+MWTwzqiH+nZqPg6nV8+dtFPHrLEu6fTpeiuKoWQd4y/OW2UDeNkoiIiIiIiFoam7MPb731Fj777DM89thjThgOEbmTslaDPTmXAQB/H+CYGXI31zksqa5FgJcUOZeVTSY5qVRpIBWL0SXIB/mvxONMaXWr/Z8tXjIJnh/RGfN+Oo1lu7Px0O3GHdxX32iO88gdEZBJmsbMVyIiIiIiImr+bP6FKZfLMWTIEGeMhYjcRKnSQKXRoaJGg5NzR+LnJwdhcGRbh51fX+fw2IUKRP0rFdM2HXfYue1Ro9Zi+Z5chL62E52W7kLEkl347lQJatRadw/NbZ6Ji0RbTynOXlLimxNFhu0XK65j2x91NTq5vJuIiIiIiIgcyeYE5ezZs7Fy5UqHXHzRokUQiURGr27dujX4nq+++grdunWDQqFAr169sG3bNqP9giBgwYIFCAsLg6enJxISEpCdne2Q8ZL76RNppdW1UGl0UKo0Nu2n+m5O0nVYkoKIJbuQVnAFtRqdw68V16ktLitV+O1iBYoqaxx+flsoVRos252DJSlnDZ3Gy6+rsSTlLF7fndNqY6eNQoLZw6IBAP9KPQudTgAArDly3tDZvWtw4zu7ExEREREREd3K5gTl4cOHsW7dOkRHR+Mvf/kL7rnnHqOXrXr06IGioiLDKy0tzeyxBw4cwOTJkzFt2jRkZGRg0qRJmDRpEk6ePGk4Zvny5VixYgU++ugjHDp0CN7e3khKSkJNjXuTIWS/mxNpoYt2IvS1nXhzT65htpul/VSf+SRdtlOSdCFt5BgQ4Q8A2Ha61KHntpVULMbKtHyT+1ak5UMqbr1LmGcNjUQbuQRZRVXYebYUOp2ArVl1symfuJOzJ4mIiIiIiMixbK5B6e/v36hEpNkBSCQIDbWu2cJ7772HsWPHYu7cuQCAJUuWICUlBe+//z4++ugjCIKAd999F/Pnz8fEiRMBAOvXr0dISAi2bt2KBx980GHjJtdSqjRYvicXS27qBl1+XW3oDv38iGi8tS/P7P65ozrDW2Zfw5eWyFKS7uX4Lg6/5vjuwThyvhzb/yjBNDcuFS6vURuSsvX2XVejokaNIB+5i0fVNLT1kmFRYiyi23ljeHQgSqprsW/GEOzJuYz4mEB3D4+IiIiIiIhaGJszNmvWrHHoALKzsxEeHg6FQoG4uDgsW7YMHTuaTlqkp6fj+eefN9qWlJSErVu3AgDy8/NRXFyMhIQEw34/Pz8MGjQI6enpZhOUtbW1qK2tNfy5srLSzk9FjtZQIu2/v13Ay/FdXJ5oawnckaSb0D0Er+08i5Szl6HS6FzSbEXfCKe8Rg1/hRTX1Vq0kUvg7yk1+fn9PaXwU0idPq6m7OnBkViWmo3HN2Xe1H09Egldgtw9NCIiIiIiImph3LqGcdCgQVi7di127NiBDz/8EPn5+Rg2bBiqqqpMHl9cXIyQkBCjbSEhISguLjbs128zd4wpy5Ytg5+fn+EVERFhz8ciJ2gokeYp9cDV65YTbVSfv0IKf0/TiThnJen6t/dDSBs5qmo12J9f5vDz38rU0v+39uWiRqPFzCGRJt/z3NAoqHWOr8HZXChVGry+OwdLd2W7ZOk/ERERERERtW42JyijoqIQHR1t9mWLcePG4f7770fv3r2RlJSEbdu2oby8HJs3b7Z1WHZJTk5GRUWF4XX+/HmXXp8sayiRdl2tRVtP1yfaWgK1TofnhkaZ3OesJJ1YLMK4rsEAgJ+cXIfSXI3Npbuy8fnRC5g3ugsWjIk1xI6/pxQLxsRi3uiYVl0SgPU5iYiIiIiIyJVs/gU+Z84coz+r1WpkZGRgx44dhtqQjeXv74/Y2Fjk5OSY3B8aGoqSkhKjbSUlJYYalvq/lpSUICwszOiYvn37mr2uXC6HXN46a801FyqtDjOHRGLprvod2af274AajRbPDY0y1Jy8mT7RJnPvhOEmyVsmwbzRMdAJAt7/tcCwlPe5oVGYNzoGCqmHU647vnsw1h49j22nS/D23T2ccg2g4UTbop1n8XRcJOaO6oyX47ugokYNP4UUap3OaZ+7uWB9TiIiIiIiInIlmxOUs2fPNrl91apVOHr0qF2Dqa6uRm5uLh5++GGT++Pi4pCammqUJE1JSUFcXByAutmdoaGhSE1NNSQkKysrcejQITzzzDN2jY3ca/vpEsy6MdPPXCJt3ugYAHUzvPT7Zw6JdGqirSUQi4ABEf44/2oCqmvrZqM6O0k3JjYIErEIZy8pkXNZiZhAb6dcp9yKpf/6RJv+r0xk/2/GMutzEhERERERkSs47Jf4uHHj8M0339j0nhdeeAH79u1DQUEBDhw4gL/+9a/w8PDA5MmTAQCPPPIIkpOTDcfPnj0bO3bswFtvvYU//vgDixYtwtGjRzFz5kwAgEgkwpw5c7B06VJ8//33yMrKwiOPPILw8HBMmjTJUR+VXExZq8Hs705hxAcHMLlfexQvTETJokQUL0zE3FGdDYk0hdQDc0d1RvHCRBQvSsT5VxPQr70fNmUWuvkTOIZSpYFKo0NpdS1UGp3D6gDuz7+CiWuOYNB7+xHsI4NMInb68mY/TymGRQUAALadLrFwdOMcL6yAt9yDS/8bwR1L/4mIiIiIiKj1cliC8uuvv0ZAQIBN77lw4QImT56Mrl274m9/+xvatWuHgwcPIiiorkvsuXPnUFRUZDh+8ODB+PLLL/Hxxx+jT58++Prrr7F161b07NnTcMyLL76IWbNmYfr06RgwYACqq6uxY8cOKBQKx3xQcrm3f8lDcVUtajU6RLfzgkwiRpCP3GQizVsmgUwiRrCPHFtOFOHedUcx+7uTKK6scdPoHcNUo5c39+SiRq21+9zbbtSBHNixLUQikd3ns9b47iFG17fHrcnbk0WVePjLDOw6e5mNcBpBv/Sf9TmJiIiIiIjIFUSCIAi2vKFfv35GSQxBEFBcXIxLly7hgw8+wPTp0x0+SFerrKyEn58fKioq4Ovr6+7htGolVbXo8noqqmu12PBQfzzQt73V79XpBMStTMOR8+V45PYOWDu5nxNH6jxKlQbL9+RiiYn6mgvGxGLuqM52JYx6LN+D06XV2PTw7bi/T7g9Q7XJH6VVuG35Xsg8xLi8OAk+8sZ9hhq1Fst252DlLUv7Zw2NwqeHzmHO8Gi8sTvHaOm/s2tsthRKlQZSsdioPieTk0RERERERGQNW/JrNv/SvHWptFgsRlBQEEaOHIlu3brZejqiBi1OOYvqWi0GRPjj/t62Jc/EYhFW/rUn7lyRhvXHLuDJOzthSJRts3ybAksdlV+O79LocxdcuYbTpdXwEIswJjao0edpjK5BPohu54W8smtIzb6MiT1DbT6HqeStvku3SAS8OCoGnjeW/rMRju30yUjW5yQiIiIiIiJnsjlBuXDhQmeMg6ieM6XV+PjgnwCANyZ0h1hs+/LjgR3b4u8DO+Kzw+cw89ssHJ0zHB6NOI87ObOj8vY/6pZXD+7U1mytRmcRiUQY3z0E76fl46fTJY1KUDaUvF2ZVoBX4mMBMNFGRERERERE1JTxVzo1Wa+nZkOrE3BX9xCMjAls9HmWje+GgR39sSixK9RaxzeZcTZ9R2WT++xs9LL9Rv3Hsd2CG30Oe4y/cd3tf5TCxmoTAKxL3hIRERERERFR02Z1glIsFsPDw6PBl0TC2mRkH32zk6LKGrx/by98+9gAvH13D7vOGeQjx87pd+LYhXKEL05xeJMZZ1PrdGYbvcwcEomMixVQabQ2d/muUWuxO+cyAGB8d/ckKEd2bgcvqQcuVtTgRFGlze93ZvKWiIiIiIiIiFzD6ozit99+a3Zfeno6VqxYAR074pId9J2qjZqdDI1EUlf7aiMqVRq8tS8PS3dlG7aVX1dj8Y26hfY2mXG2MqUKs4ZGAQDe/7XAcG9mDY3EzCFReHxjJtZP6Yf39ucb3TtLjWD251/BNbUW4b4K9A5zTzMohdQD8V0C8cPvJfjpdCn6hPvZ9H61TofnhkYZ/lneTN+lm8u5iYiIiIiIiJo2q7MyEydOrLftzJkzmDdvHn744QdMnToVixcvdujgqPUw2+wkJRtiiOxKIjqzyYwr/GdfHnadvYSP7+uN+QmxRo1ejhdW4pnBkXj3F9sTsNtOlwCoW94tErmvLuf47sH44fcSHMgvA2DbPwtvmQQvjOwMnSAYJW/ZpZuIiIiIiIio+WhUxqewsBALFy7EunXrkJSUhMzMTPTs2dPRY6NWxJlJRGc2mXG2q9dUWHP4HJQqLWo0OsgkYqNGL4MjA1Cr0eLhDRkm39/Qvdtxo0HOuG6u7d59q7t7hCK0jQIJsYEoqapFW8+65Ku1CekPDxSgfwd/XFwwBtW1GnbpJiIiIiIiImpmbFr7WFFRgZdeegkxMTE4deoUUlNT8cMPPzA52YrYWufQWs5sdtKc6xR+fLAuOdk7zBfxXUw3Cqqo0dh87/LKlDhzSQmJWISELu5NULb1lOLYhXJELNmFsNdsrw/6zYki3LP2CH74vRhBPnLIJOImvWSfiIiIiIiIiIxZnaBcvnw5oqOj8eOPP2LDhg04cOAAhg0b5syxUROjrxEZ+tpOhzeacWYSUV+n0BR9ncKmSKXRGWaV/mN4tNll2I25d9tvzJ4cEhkAPzPvdQWlSoNlu3OwdFe2IcmqX57++u4ciwnwqhoNjl2sAADc2bGt08dLRERERERERI5n9TSjefPmwdPTEzExMVi3bh3WrVtn8rgtW7Y4bHDUdJirEemoRjNqnQ6zhkYZnV/P3mYn3jIJ5o2OAVC35NnQgGdIZJOuU7j5eCEKK2sQ5ivH5H7tzR7XmEYx+uXdY7u5p3u3nr1L+w/8eQVanYDItp7o2NbLGUMkIiIiIiIiIiezOqP0yCOPuLWRBrmXsxvNeMskmD0sCoKTmp0opB6YO6ozXo7vgvIaNbxlHth55hKKKmsQ1c7brnM7gyAIeHtfLgBgxpAoyCTmk7MNJWBfGh0Dz1vuXY1ai905lwHUNahxJ3vrg+7LLQMADI9u55TxEREREREREZHzWZ2gXLt2rROHQU2dpUTS1etqhLSRQ6nSQCoWo7xGDX+F9c1ODv15FY9vysSy8d1RtDARlTd1qnbUDEf9OIJ95Jj632PYkFGIeaNj8O/x3R1yfkfak1OGzMJKeEk98HRcJ4vHm0vArvo1Hy+MjDE6dl9eGa6rdWjvp0DP0DbO+ghW0S9PNxVb1izt3593I0HZmQlKIiIiIiIiouaqcWtmqdWxVOewjdwDV6+psHxPTqNqVP5nby7+KK3G1pNFkN/oVO3MZif39AoDAHx+7AK0OsEp17DHO7/UzZ58dEAEArxkVr3HWyaBTCJGsI8cv+Zfwb3rjmLeT6dx5Fy50XHbTuu7dwe7fVa0PfVBr6k0OHy+HAAwgjMoiYiIiIiIiJotJijJKg0lkmYOjURptQrv/JKHJSm2NzvJvlSNLSeLAAD/HNHZ8YM34a7bQhDgJcXFihrsyr7kkmtaK69MiUPnyiES1TXHaYzErsGY0q89dAIwbXMmVJr/Jfr09SfHubn+JPC/5ekLxsQaEuD+nlK8OiYW80bHNJigPvhnOdRaAe39FIhux/qTRERERERERM0VE5RkFW+ZBHOGR2N+QhejRNKCMbF4eXQXhPkq8P6vBSbfuyItH1Kx+VB7+5c8CAIwoXsweob5OmP49cglHobGM+uOnHfJNS1RqjRQaXSQeoiR/0o89s8YgpjAxtfHfHdSDwR5y3CyuAr/Ts0GUJf8lEvECG0jR3yXQEcN3S765enFCxNxbn4Czr+agLtvC7G4tH9f3v/qT7p7JigRERERERERNZ5z1s9Si1OmVGHM/6VjQWJXFC4cg6oajVGNyNLq2kY1OympqsXaGwnCF0a6Zvak3mMDIrDq1wJ8e7LY0FjGXWrUWizfk4uVNzW5mTU0Cv3b+zW6Bmegtxwr/9oLD35xDFuyivDM4E4I91Xgu78PRIiPHAKaztJ2/UzJvLJruO+do5B5iHFufgLEYvOJR0P9yegAl4yRiIiIiIiIiJyDMyjJKh+lFyCzsBKLd56B3KN+jUhLNSrNNTt5/9d81Gp0GBjh7/JOzP3b+6FnaBvUanTYlHnRpde+mVKlwbLdOViSctZoefwSK5bHW3J/nzDMHBKJ1KfjsOrXAoQvTkHnf6eiw5IUq+uDutKgTv5QaXUorKzBwXNXzR5Xq9Hi4J91+0ewQQ4RERERERFRs8YEJVlUq9Fi1Y3l28+P6GxyOW2DNSqHRJpsdlJdq8EHN847d5Tp8zqTSCTCo3dEAADWHb3g0mvfTCoWY2Vavsl9lpbHWyISifBaUlesTMvH0l221wd1NbnEA3ffFgIA+PpEkdnjDp8rR41Gh5A2cnQN8nHV8IiIiIiIiIjICZigJIu+/O0iiqtq0d5PgQf6hps8xlyzk/kJXTBraBS+/K1+AnD14XO4el2NmEBvTOoZ5tTPYM7U/u3hIRbh4J9X8UdplVvGUF6jtrg83h7eMkmj64O6wz2962Jhy4kiCILpZei/6Jd3RwWw/iQRERERERFRM8calNQgQRDwzi95AIBZQ6Mg9TCfzNI3O3k5vgsqatTwU0iRf+UaRnxwAGcvVSMqwBsJsUEAALVWh3f21Z33+eHR8Gig1qAzhfoqMK5rMH48XYJ1Ry9g2fjuLh+Dfnm8qSRlQ8vjrWVNAtRUfVB3SeoaDG+ZB86VX8fR8xUY0NG/3jH6BOUwF5cFICIiIiIiIiLHa1pTp6jJ2Xn2Ek4WV8Fb5oHpd3ayeLy3TAKZ5H81KrsG+2BwZAB0AjD5i2P488q1uvOeKcU1tRZB3jI8OiDC2R+jQY8O6AAA+PzoBWh1rm8co9bpMGtopMl9zw2NMrk83haNrQ/qLp5SD0zorl/mXVhvv1qrw4EC1p8kIiIiIiIiaimYoKQGvb0vFwDw94EdG93l+v2/9sQdHfwQ5CNH3pVrUGl06Bnmi/xX4pH6dBw8G9ml2lH+clso2nlJUVhZg13Zl1x+/UvVtZg5JArzE7oYLY9fMCYW80bHGBoRNVZD9UEdkQB1hnv1y7yz6i/zPnahAkqVFgFeUvQIaeOO4RERERERERGRA3GJN5l1orASKWcvQywC5gyLbvR5FFIPbH18AGQSMVbsz8e9646i/Loa/p5SPDc0CjGB3lC4MUkpk4gxuX8HvJ+Wj29OFCGpa7BLrz/3h9M4VVKFj+/rjfkJsYbl8WqdziH3RV8fFKirOXnzvZ83Osat996ccd2C4SkVI7fsGo4XVqJvez/DPkP9yeh2ELupNAAREREREREROQ4TlGTWuzdqT97TKwxR7bzsOpefpxTL9+Rg6a5swzZ9J2mgrou3vTMF7fH0nR0RHxOIhNhAlFTVoq1nXYLQ2WM6UHAF32QVQSyqmzWpXx4PADIHTnA2VR/UUQlQZ/CRSzC2azC+PVmMr08UGScoc/X1JwPcNTwiIiIiIiIiciAu8SaTLlXX4uezpQCA50d0tvt8UrEYK9MKTO5rCp2ko9t549iFckQs2YWw13Yi9LWdeHNPLmrUWqddUxAEzP3hdwDA4wM7omeYr9OuBdSvD+rOhLA19Mu8vzlRaFjmrdUJSCu4AgAYwQY5RERERERERC1C085QkMspVRpIxWLUanQ4O280jpwrx52d2tp93qbcSVqp0mD5nly7Z3fq7115jRr+CsszMLdkFSH9z6vwknpgcVJX+z9IC3PXbSGQeYhx5pISv5dUo0doGxwvrEBljQZ+Cgn6hPtZPgkRERERERERNXmcQUkGNWotlu/JRehrO9Fx6S5ELNmFPbllDplF2JQ7SdfN7sw3uc/a2Z0337vQRZZnYKo0OiRv+wMA8M+RnRHmq2j8B2ihfBVSJMYGAQC+OVEEANh3o/7k0KgAeLD+JBEREREREVGLwAQlAaib/bdsdw6WpJw1zHQsv67GkpSzeH13DpQqjV3nb8qdpK2Z3dkQc/ducQP37qP0AuRcViKkjRxzR9q/hL6lMizzzioE8L/6k8O5vJuIiIiIiIioxWgyCcrXX38dIpEIc+bMMXvMyJEjIRKJ6r0mTJhgOOaxxx6rt3/s2LEu+ATNmyNmETZE30l6wZhYw0xKf08pFoyJxbzRMW6th2jv7E5b711ljRof/Fp3/KLEWPjIWWnBnLt7hEAiFiGrqAp/lFZhf35d/UkmKImIiIiIiIhajiaRGTly5Aj+7//+D717927wuC1btkClUhn+XFZWhj59+uD+++83Om7s2LFYs2aN4c9yuXtqGzYnrqgR2VQ7Setnd+prTt5MP7uzoY7alu7d5WsqBHpJodYJkIrFqKzR4NjzI3Cg4CpGdWairSFtvWSI7xKIn89cwru/5CPcV4E2cgn6d2D9SSIiIiIiIqKWwu0JyurqakydOhWffPIJli5d2uCxAQEBRn/euHEjvLy86iUo5XI5QkNDHT7WlsxPIYG/p9Rkos2RNSL1MyX1yc6GEn+uop/dCdTNeCy/roa/pxQzh0Ri3ugYiwlU/QxMc/fOXyFBVa0WK9LysDKtwHD+WUOjMCwqABL35mebvL8PiMDTcZFIiA1EabUKIT5yqLQ6SD3cHztEREREREREZD+3/8KfMWMGJkyYgISEBJvfu3r1ajz44IPw9vY22r53714EBweja9eueOaZZ1BWVtbgeWpra1FZWWn0ak3+KK3C7uzLmDkk0uR+d9eIdAX97M7ihYkoWpiI868m4PYO/qjVWG4QpNbpMMtMfc1ZQyNxWanCe/vzsCQl2yn1PVu6u3qE4tiFckQs2YXO/05FhyUpDTYgIiIiIiIiIqLmxa0zKDdu3IjffvsNR44csfm9hw8fxsmTJ7F69Wqj7WPHjsU999yDqKgo5Obm4uWXX8a4ceOQnp4ODw/TU9WWLVuG1157rVGfobnLvaxEwkcH4auQYP+MIRCLREazCJ8bGmXVLMKWQD+7M9hHhtEfpmNfXhlW3dMLzwyOtPi+fwyPhiAIeP/Xgnr3TiwS4f1fC0y+d0VaPl6O7+LgT9JyKFUaLN+Ti6W7sg3b9A2IAGDuqM5urV9KRERERERERPZz2y/78+fPY/bs2UhJSYFCobD5/atXr0avXr0wcOBAo+0PPvig4e979eqF3r17o3Pnzti7dy/i4+NNnis5ORnPP/+84c+VlZWIiIiweUzNgVKlgVQsRvmNGpDZl5XwVUggEYsgFqFJ1oh0NZFIhLt7hmBfXhnWHTlvMUFZcV2N+A8P4NXErihaOAaVNRqje1daXev0+p4tlaUGREzuEhERERERETV/blvifezYMZSWlqJ///6QSCSQSCTYt28fVqxYAYlEAq3W/PJNpVKJjRs3Ytq0aRavEx0djcDAQOTk5Jg9Ri6Xw9fX1+jVEtWotVi+Jxehr+1E6KKdCHttJw4UXMH+GUOw+5nBaOslg7dMAplEjCAfOWQScaudnTa1XwdIxCIcPl+O34urGjz2y4yLyCisxPztpyHzqH/v7O0S3ppZ07yJiIiIiIiIiJo3tyUo4+PjkZWVhczMTMPrjjvuwNSpU5GZmWl2OTYAfPXVV6itrcVDDz1k8ToXLlxAWVkZwsLCHDn8Zkep0mDZ7hwsSTlrVAdx6a5srEzLh6fU7eVIm5TgNnKM7x4MAFh39LzZ4wRBwMcH/wQAPHlnJ4hEonrH6LuEm9Ia6nvag8ldIiIiIiIiopbPbVmpNm3aoGfPnkYvb29vtGvXDj179gQAPPLII0hOTq733tWrV2PSpElo166d0fbq6mrMnTsXBw8eREFBAVJTUzFx4kTExMQgKSnJJZ+rqbK0VFYqZoLyVo/eUbfM/4vfLkCjNZ1EPHq+AscLKyGXiPHw7R1MHqPvEr5gTKwh2ebvKcWCMbGYNzqm1c5StQaTu0REREREREQtX5POjJw7dw7iWxJnZ86cQVpaGnbu3FnveA8PD5w4cQLr1q1DeXk5wsPDkZiYiCVLlkAub901/qxZKss6iMYmdA9BOy8piiprkXL2EsZ1D6l3zMeH6mZP3t87DAFeMrPn0ncJb+31PW2lT+4CaLXNm4iIiIiIiIhaOpEgCIK7B9HUVFZWws/PDxUVFS2mHqVKo0PoaztNJin9PaUoXpgImYSzKG81e+tJrEzLx9/6hGPjw7cb7ausUaP94hQoVVrse3YwhkW3M3MWspe+udPNyV3OPCUiIiIiIiJqumzJrzEj1UpwqWzjPD6gbpn31pPFuHpNZbRvQ8ZFKFVadAv2wdCoAHcMr9Vg8yYiIiIiIiKilosJylaCdRAbp297P/QJ94VKq8OGjEKjfZ8cPAcAePLOjiab4xARERERERERkWXMSrUirIPYOI/eEYHnvz+FdUfP49khkQCAYxfK8dvFCsg8xHjk9gj3DpCIiIiIiIiIqBnjDMpWhktlbTelX3tIxCIcOV+OU8VVAICPD9Y1x7mvdxjaeZtvjkNERERERERERA1jgpLIguA2ckzoHgwA+PpEIZS1Guw8cwlA3fJuIiIiIiIiIiJqPHbxNqEldvEm++zKvgRlrRYJsYEov66Bv6cEB/KvICE2iPUniYiIiIiIiIhuYUt+jet7iawwJDIAy1Kz8fimTJRfV8PfU4pZQyMxLLoda3gSEREREREREdmBCUoiC5QqDZbvycXSXdmGbeXX1ViSkg0RRJg7qjNreRIRERERERERNRJrUBJZIBWLsTIt3+S+FWn5kIr5rxERERERERERUWMxs0JkQXmNGuXX1ab3XVejosb0PiIiIiIiIiIisowJSiIL/BVS+HtKTe/zlMJPYXofERERERERERFZxgQlkQVqnQ7PDY0yue+5oVFQ63QuHhERERERERERUcvBzh5EFnjLJJg3OgZAXc1JfRfv54ZGYd7oGHbxJiIiIiIiIiKyg0gQBMHdg2hqKisr4efnh4qKCvj6+rp7ONREKFUaSMViVNSo4aeQQq3TsXs3EREREREREZEJtuTXmF0hspI+GRnkIwcAyFghgYiIiIiIiIjIbsywEBERERERERERkdswQUlERERERERERERuwwQlERERERERERERuQ0TlEREREREREREROQ2TFASERERERERERGR2zBBSURERERERERERG7DBCURERERERERERG5jcTdA2iKBEEAAFRWVrp5JERERERERERERM2PPq+mz7M1hAlKE6qqqgAAERERbh4JERERERERERFR81VVVQU/P78GjxEJ1qQxWxmdTofCwkK0adMGIpHI3cNxuMrKSkREROD8+fPw9fV193ColWDckTsw7sjVGHPkDow7cgfGHbkaY47cgXFnH0EQUFVVhfDwcIjFDVeZ5AxKE8RiMTp06ODuYTidr68v/wUjl2PckTsw7sjVGHPkDow7cgfGHbkaY47cgXHXeJZmTuqxSQ4RERERERERERG5DROURERERERERERE5DZMULZCcrkcCxcuhFwud/dQqBVh3JE7MO7I1Rhz5A6MO3IHxh25GmOO3IFx5zpskkNERERERERERERuwxmURERERERERERE5DZMUBIREREREREREZHbMEFJREREREREREREbsMEJREREREREREREbkNE5RERERERERERETkNkxQNiG//PIL/vKXvyA8PBwikQhbt2412l9SUoLHHnsM4eHh8PLywtixY5GdnW3yXIIgYNy4cSbPk5qaisGDB6NNmzYIDQ3FSy+9BI1GY3F8e/fuRf/+/SGXyxETE4O1a9faNH5qmhwRdyNHjoRIJDJ6Pf3000bHnDt3DhMmTICXlxeCg4Mxd+5cq+Luq6++Qrdu3aBQKNCrVy9s27bN5vFR0+KKmDt+/DgmT56MiIgIeHp6onv37njvvfesGp+lmHvsscfqXXvs2LGNuxnkMq76rtMrKytDhw4dIBKJUF5ebnF8luJuy5YtSExMRLt27SASiZCZmWnLxyc3cVXc3bpfJBJh48aNFsfHZ2zL44qYW7t2rcmYE4lEKC0tbXB8fMa2TK76ruPvWLqZo/In6enpGD16NLy9veHr64vhw4fj+vXrhv1XrlzB1KlT4evrC39/f0ybNg3V1dUWx2cp7qqqqjBnzhx06tQJnp6eGDx4MI4cOdKoe9FSMEHZhCiVSvTp0werVq2qt08QBEyaNAl5eXn47rvvkJGRgU6dOiEhIQFKpbLe8e+++y5EIlG97cePH8f48eMxduxYZGRkYNOmTfj+++8xb968BseWn5+PCRMmYNSoUcjMzMScOXPwxBNP4Oeff7Zq/NR0OSrunnzySRQVFRley5cvN+zTarWYMGECVCoVDhw4gHXr1mHt2rVYsGBBg2M7cOAAJk+ejGnTpiEjIwOTJk3CpEmTcPLkSZvHR02HK2Lu2LFjCA4OxhdffIFTp07hlVdeQXJyMt5///0Gx2Yp5vTGjh1rdO0NGzbYcUfIFVwRdzebNm0aevfubdXYrIk7pVKJoUOH4o033rDhU5O7uTLu1qxZY3TMpEmTGhwbn7Etkyti7oEHHjDaV1RUhKSkJIwYMQLBwcFmx8ZnbMvlirjj71i6lSPiLj09HWPHjkViYiIOHz6MI0eOYObMmRCL/5cqmzp1Kk6dOoWUlBT8+OOP+OWXXzB9+vQGx2ZN3D3xxBNISUnB559/jqysLCQmJiIhIQEXL150wN1ppgRqkgAI3377reHPZ86cEQAIJ0+eNGzTarVCUFCQ8Mknnxi9NyMjQ2jfvr1QVFRU7zzJycnCHXfcYXT8999/LygUCqGystLseF588UWhR48eRtseeOABISkpyarxU/PQ2LgbMWKEMHv2bLPn3bZtmyAWi4Xi4mLDtg8//FDw9fUVamtrzb7vb3/7mzBhwgSjbYMGDRKeeuopm8ZHTZezYs6UZ599Vhg1alSDx1iKOUEQhEcffVSYOHGiTdempsXZcffBBx8II0aMEFJTUwUAwtWrVxs83pq408vPzxcACBkZGRbHQU2LM+OuMf/dxWdsy+eqZ2xpaakglUqF9evXN3gcn7Gtg7Pijr9jqSGNjbtBgwYJ8+fPN3ve33//XQAgHDlyxLBt+/btgkgkEi5evGj2fZbi7tq1a4KHh4fw448/Gh3Tv39/4ZVXXmn4w7ZgnEHZTNTW1gIAFAqFYZtYLIZcLkdaWpph27Vr1zBlyhSsWrUKoaGhJs9z8zkAwNPTEzU1NTh27JjZ66enpyMhIcFoW1JSEtLT0xv1eah5sDbuAOC///0vAgMD0bNnTyQnJ+PatWuGfenp6ejVqxdCQkIM25KSklBZWYlTp06Zvb6luLNlfNQ8OCrmTKmoqEBAQECDx1j7Xbd3714EBweja9eueOaZZ1BWVmbxs1HT5ci4+/3337F48WKsX7/e6P++N4TP2NbJ0d93M2bMQGBgIAYOHIjPPvsMgiA0eH0+Y1sfZz1j169fDy8vL9x3330NXp/P2NbJUXHH37FkC2virrS0FIcOHUJwcDAGDx6MkJAQjBgxwigu09PT4e/vjzvuuMOwLSEhAWKxGIcOHTJ7fUtxp9FooNVqTcZ0a37GMkHZTHTr1g0dO3ZEcnIyrl69CpVKhTfeeAMXLlxAUVGR4bh//OMfGDx4MCZOnGjyPElJSThw4AA2bNgArVaLixcvYvHixQBgdJ5bFRcXGyWXACAkJASVlZVG9RmoZbE27qZMmYIvvvgCe/bsQXJyMj7//HM89NBDhv3m4ke/zxxz79O/x9rxUfPhqJi71YEDB7Bp0yaLyzEsxRxQt/Rs/fr1SE1NxRtvvIF9+/Zh3Lhx0Gq1jfzU5G6Oirva2lpMnjwZb775Jjp27Gj19a2JO2p5HPl9t3jxYmzevBkpKSm499578eyzz2LlypUNXp/P2NbHWc/Y1atXY8qUKfD09Gzw+nzGtk6Oijv+jiVbWBN3eXl5AIBFixbhySefxI4dO9C/f3/Ex8cbalUWFxfXK10hkUgQEBDQqN+x+rhr06YN4uLisGTJEhQWFkKr1eKLL75Aenp6q37GMkHZTEilUmzZsgVnz55FQEAAvLy8sGfPHowbN84wQ+P777/H7t278e6775o9T2JiIt588008/fTTkMvliI2Nxfjx4wHAcB4fHx/Dy1zxf2odrIk7AJg+fTqSkpLQq1cvTJ06FevXr8e3336L3Nxcq65z7tw5o7j797//7dDxUfPhjJg7efIkJk6ciIULFyIxMRFA42MOAB588EHcfffd6NWrFyZNmoQff/wRR44cwd69e+3+/OQejoq75ORkdO/e3ewPeXvijloeR37fvfrqqxgyZAj69euHl156CS+++CLefPNNAHzG0v844xmbnp6O06dPY9q0aYZtfMbSzRwVd/wdS7awJu50Oh0A4KmnnsLjjz+Ofv364Z133kHXrl3x2WefWX2txsbd559/DkEQ0L59e8jlcqxYsQKTJ09u1c9YibsHQNa7/fbbkZmZiYqKCqhUKgQFBWHQoEGG6ca7d+9Gbm4u/P39jd537733YtiwYYYH+/PPP49//OMfKCoqQtu2bVFQUIDk5GRER0cDgFFnUF9fXwBAaGgoSkpKjM5bUlICX19fi/+3lJo3S3FnyqBBgwAAOTk56Ny5M0JDQ3H48GGjY/TxFBoaivDwcKO40y/DNRd3N5cvaMz4qGlzRMzp/f7774iPj8f06dMxf/58w3Z7Yu5W0dHRCAwMRE5ODuLj4236rNR0OCLudu/ejaysLHz99dcAYFhiGxgYiFdeeQWvvvqqw+KOWgZHft/desySJUtQW1vLZywZcXTMffrpp+jbty9uv/12wzY+Y+lWjoo7/o4lW1iKu7CwMADAbbfdZvS+7t2749y5cwDq4qe0tNRov0ajwZUrVwzfXY2Nu86dO2Pfvn1QKpWorKxEWFgYHnjgAUM8t0atNzXbjPn5+SEoKAjZ2dk4evSoYTn3vHnzcOLECWRmZhpeAPDOO+9gzZo1RucQiUQIDw+Hp6cnNmzYgIiICPTv3x8AEBMTY3jppzPHxcUhNTXV6BwpKSmIi4tz8qelpsJc3Jmijz39l35cXByysrKMvtxTUlLg6+uL2267DRKJxCju9P8ha0vc2TI+ah7siTkAOHXqFEaNGoVHH30U//rXv4yOd0TM6V24cAFlZWVG16bmy564++abb3D8+HHDM/jTTz8FAOzfvx8zZsxwaNxRy2Lv952pY9q2bQu5XM5nLJnkiJirrq7G5s2bjWZPAnzGknmOiDv+jiVbmYu7yMhIhIeH48yZM0bHnz17Fp06dQJQFz/l5eVGdU53794NnU5nSKLbG3fe3t4ICwvD1atX8fPPP7fuZ6ybm/TQTaqqqoSMjAwhIyNDACC8/fbbQkZGhvDnn38KgiAImzdvFvbs2SPk5uYKW7duFTp16iTcc889DZ4TJrqQLV++XDhx4oRw8uRJYfHixYJUKrXYqSwvL0/w8vIS5s6dK5w+fVpYtWqV4OHhIezYscPq8VPTZG/c5eTkCIsXLxaOHj0q5OfnC999950QHR0tDB8+3HCMRqMRevbsKSQmJgqZmZnCjh07hKCgICE5ObnBsf3666+CRCIR/vOf/winT58WFi5cKEilUiErK8twTGP+vSD3ckXMZWVlCUFBQcJDDz0kFBUVGV6lpaUNjs1SzFVVVQkvvPCCkJ6eLuTn5wu7du0S+vfvL3Tp0kWoqalxwt0iR3FF3N1qz549VnXxtua7rqysTMjIyBB++uknAYCwceNGISMjQygqKrLvxpBTuSLuvv/+e+GTTz4RsrKyhOzsbOGDDz4QvLy8hAULFjQ4Nj5jWyZXftd9+umngkKhsPgdp8dnbMvlqrjj71i6mSPyJ++8847g6+srfPXVV0J2drYwf/58QaFQCDk5OYZjxo4dK/Tr1084dOiQkJaWJnTp0kWYPHlyg2OzJu527NghbN++XcjLyxN27twp9OnTRxg0aJCgUqkceJeaFyYomxD9D5lbX48++qggCILw3nvvCR06dBCkUqnQsWNHYf78+UJtbW2D5zSVoBw1apTg5+cnKBQKYdCgQcK2bdusHl/fvn0FmUwmREdHC2vWrLFp/NQ02Rt3586dE4YPHy4EBAQIcrlciImJEebOnStUVFQYXaegoEAYN26c4OnpKQQGBgr//Oc/BbVabXF8mzdvFmJjYwWZTCb06NFD+Omnn4z2N+bfC3IvV8TcwoULTV6jU6dOFsfXUMxdu3ZNSExMFIKCggSpVCp06tRJePLJJ4Xi4mKH3R9yDld915m6pjU/3i19161Zs8bk+BcuXNiY20Eu4oq42759u9C3b1/Bx8dH8Pb2Fvr06SN89NFHglartTg+PmNbHld+18XFxQlTpkyxaXx8xrZMroo7/o6lmzkqf7Js2TKhQ4cOgpeXlxAXFyfs37/faH9ZWZkwefJkwcfHR/D19RUef/xxoaqqyqrxNRR3mzZtEqKjowWZTCaEhoYKM2bMEMrLyxt9P1oCkSDcKJBERERERERERERE5GKsQUlERERERERERERuwwQlERERERERERERuQ0TlEREREREREREROQ2TFASERERERERERGR2zBBSURERERERERERG7DBCURERERERERERG5DROURERERERERERE5DZMUBIREREREREREZHbMEFJREREREREREREbsMEJREREREREREREbkNE5RERERERERERETkNv8P7JPddmrvcNMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# compute the logarithm\n",
    "logy = np.log(y)\n",
    "\n",
    "plot_series(logy)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this looks additive now!\n",
    "\n",
    "ok, what next - deaseasonalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "# apply this to y\n",
    "# wait no, to logy\n",
    "\n",
    "seasonal_result = seasonal_decompose(logy, period=12)\n",
    "\n",
    "trend = seasonal_result.trend\n",
    "resid = seasonal_result.resid\n",
    "seasonal = seasonal_result.seasonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>, <AxesSubplot: ylabel='trend'>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series(trend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>, <AxesSubplot: ylabel='seasonal'>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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11gq0r7GUXWYlit3bwj6E/dVV+Ip7erK9FSgBqdqVOnSWqgemjUalwpVXXokrr7yy7LWnn356wn/v3LnT/gciHKfUcVBLr0OOiCRogreIXRr1bCirz1oGJ4g/V9fEokOUtaibmhrO+SDnQcTvwXhWwMB4tqaDh6iNEhipPd8UNpQU+baEWt2PS2mP+LB1gOYbq9ilUZe1uMbSIcoK+hJZ5AQRHpbB3IZaaywdYK2kWvfjUiJ+DwJeFpm8iIHxHBbWcPAQtVHOUzTfOItWuwOy7XcMp8j2FqG9UofOUvXAtMqoJAhAu/gzUFoqQhONFejV9kjmKPJtBYrd58QC8HurC8mTroq16N1QArSZt4JsXsC+uCzcr12XlexuBfrWWLK9lRTXWG1jfjTNI5cXbX+uAx3F7l0NAXhrNESjA6y17NIYkGIYhmxvIalcXp23tcurkN2toNhtXcc5lgKxlqB1f6PMNUOpHASR5JzcghyVxLSjW81wqj3Bt9EhylK6NS6uUb8X/kI3tUFqZGQafZsaGvNWsnNYm3MeoAOsleweSUOSgBDnUZu2VILsbi1q+bGGMU8Z3NYhSZJm/aymIKdq5g4myfZm0Wp3gIKBVrNTY6NAgBxmVqLsKxsCXjTWqABpj9K+0kq0NgoE6BxrJZJUIvFRY3/TEuLAMIAkAcN0jnUNclQS045dGjMOANrUWMlomsdomgdQe3GVI99ke6soNtKhMe80estgAbK9FZTavbbEh2x3inxbg9buxwCNeSsZTuWQzAkAgHmN1ecblmVIj9hCtGr1ARQMtBqtjQIBsr2VaNUGBSYGA6lZoHm0lh8DFIi1kr5EFpm8CJYB5taQ0ZKbBdL+xm3IUUlMO/QdogpNFhK0qTGLsrC2hX2IaNAGog2ldWhtsABQ5NtK8oKInlFt0VeAIt9Woqf8WMm4pMi3NWjtfgyUrLE05k2j2H12LIAAV13iA6ADrJXs1NhIByC7W0kuL2KvKvFBtneSnRr1cIFiQ5e8KKkJC4Rx9NieMritQ9lXzm0Igqsh8QGUBmJpf+MW5Kgkph3qhlLHIYo2NebRU34MkO2tZJehjAOKfJtlXzyDvCiB8zCYFasefQUou8xK9JQfy5FvuXSNbG8efZk2NOatQk8JLEC2t5JdBsuPaY01R8+oLPER5FjVrtWgbvfWUVxja4/5AOdBLCAnKJDTxhySJJWssXrOsWR3s+ipkALoHFsPkKOSmFakcnn0FwSFqZmOs+gpPwbI9laitfsxQJFvK+ku6Uaq6MFVg7LLrMN4YIRsb4Z4hsf+lDxvUCmms+gpgQVKbE8VI6YxUn6c5kW1VJ8wRmmDxloSHwCtsVaipzoNIKeNVYykecQzcpNRLYFYsrt16KnUAcj29QA5KolphSKCG9Mg/gyUbGqoHNA0esqPgWIZLG0ozSFJkq5Mm9LIN417c+gpgQVKD1G0qTHLTt2BEWWuJ9ubQRnzLSEO0YB2iQ8a8+ZRAlLzNWZ7KNllNM+bQxAl7B7VnmkT9nkQ5ArNAml/Ywo9TYwAoD2qjHmab8yiR5cVKClBpvnGFIrdO6J+BDVJfJDdraI0+UALbVT67TrkqCSmFaULq57oazInIJnN2/psBzp6yo8BOsBaRX+p+HMDlSs4iV7nPOnZWIee8mOAbG8VRu0+lskjm6fsMjMYXWNpnjdHbzwDXpDgZRnM0bDGys0CyfZWoARGtJQfA5TBbSWGy2ATNObNoEe+DCjafTiVQ14QbXuumYCeJkYABcDrAXJUEtMKvQtrxO9BwFuIfFM0yhTF8mO9pd9kdzModp/TEIDPq23KJttbwy6DGZV0eDVHhhfQG9cu8QGUNjIi25tBb5ZNY5CDtyCLQNll5lCcxHrL0igYaA7F7vOagpokPgCa663CqOOA7G6O8WweQ4Uzkda5nrLLrEFv+XFL2AeGKTQLTJGckxn0V0kpjYxozLsFOSqJaYXeMhGKfFuDJEn6S3TI7pagd1MDUOTbKroN6tkMpXIQRGqyYBRF4iPi96A5VFviA6D5xir0dD8GaI21ClniQ68uKzkOrECvNihAtrcK/ZpxRceBSGusYRS7NwU5NGiQ0QJojbUKtfxY45j3sAxaQ6T5bxZRlNS9JWlUTh/IUUlMK/SWRgG0obSC0Qniz5RR6SSlYvNaoci3Negtg20JccXIN2VwG0ZvgwWAIt9Woaf7sQLN9eYZHM8hxQtgGKCrMaDpM3SIsga1/FjHmKcMbmsolsFqzOoLy3bPixJGM5RdZhS91WkAZXBbhd5KHYDmeivoS2SRE0R4WAZzGvSusbS3cQtyVBLTCjOLK03wxlHs3h7xIeSr3WBBfm/R7pJEkW+j6I2+AjTmrSAviNgzlgGg/RDl9bBooci3afSWHwNAe5TGvBUYyy4j25tFcc7PjgXg99ZusADQGmsVRoKBNObNk80L2BcvrLEa9/Q+L6s20iTbG8fQGksBKUvQGwAHyPZWoOxtuhoC8Hr0ymjRXOMW5KgkphXGFlfaUJrFSPmxktWXFyWMpinybRQz0VeKfBtnz1gGgijB52HRWXCCaYE2lOZRA1IttJF3Gr2l3wDZ3gqUrD59Eh+y3dO8iGSOGhkZZZcJ21MGt3F2j6QhSUCI86A17NP8OXW+SZDtjWLMWUZnKbNIkqRbixigQKwVmBnz8UweGZ7WWDcgRyUxbUhm82pDHD0TDZXBmsfIBO/3etAQkLMvyfbGoeirOyjO+flNQbAaGywAtJm3gqLEB2XOO8lomleDSlolPgAqg7WCYlafdruH/V6EODn7kmxvnOIaS/ONk5RWSGmV+ADI9lawS6ceLkB2t4L9KR7jWdnhNa+R1lgnMZLoFAt44fNQQ143IUclMW1QNjWNQU4t/dACZZeZp1vRcNIxwQO0sTGLEfFngOxuBXob6SiQ7c2jt3EXQJFvK1A68LaFfYj4tUl8ALTGWoERiQ+AglJmEUQJu3XqJAJkdyswUqkDkO2toNtQMFC2+3CKR14QbXmuAx3F7rNifgQ4bRIfAI15KzBSLSI3C6TybzchRyUxbdhpYGEFgPYoTTJmMZLhBJC+h1n6x7PI5mXx57kaxZ8B2dEAkN3NoGxq9DRYACiD2wqKDRa0274h4AXnkbNyKPJtDL1dpxVozJvHSKNAgAIjZtk7lkZelMB5GMyKaV9jye7mUTJZ9QbAW8Nke7MYqdRpDvmgFJcM0RprCCNZfQAFA63ASKNAoGR/kyDbuwE5Kolpg5GFFaCuXVagaqro0IwDyPZmUaKvc3WIPwNFu1Pk2zjkOHCHNC+gv7Ah1JNpI0e+yfZmMJJlA9CYt4KdBjLnAbK9WXaWVIt4DEh8DCZzEEVqZGQEo4ERCoCbI57hsT8lS3zo2d94WEbVEqU9vTGMz/Nkd7MYaRQI0DnWbchRSUwbihtKndEQyi4zhSRJxQ2l3sg3bShNYTT62hL2gaHItymMBkaU+YYi38ZQyo9jAa8uiQ+gZK6nyLch1Awnchw4irzGGsv2aKUDrCmMaIMCUB02gihhhJoFGsJwlZSaXUZj3giKnFBLiEM0oF3iAwDaKJvVFKr2ud6sPrK7KQRRwu7RoiauHigY6C7kqCSmDTsN6JYBEzc1FPnWz/4Uj0Q2DwCYZzjThjaURigVm9eDh2XQGqIDrBkMZ5dFacyboXu46JzX02ABoPnGLOaziHOQJFpj9dKfyCKTF8EywNwGOkQ5iVFtUJ+XVQMpZHtjmK+SIrsbwYgGtAIFpcxhvPSb7G6G3ngGvCDByzKYo3ONJWkbdyFHJTFtUMuPDepn5UUJoxmKfOtFsXtn1I+gDvFngHRVzGLUOQ/QZt4MubyIvWMZAGZKdMjuRjDqnAfI9mYplqUZq1rI5kU1qEVoR7H7nIYAfF5923JlzFN2mTHU7sc6A1IAzTdmyPACeuP6JT4AsrtZjDrLAArEmsWsc348KyCVozVWL4rd5zUFdUl8AHSOdRtyVBLTBqOLq9/rQUOhvIE2Nvox5yyjDaUZTG0oyfaG2TOWhigBAS+LjsLGXCuU1WcOtTTKwJhvI9sbRpIkw5k2Yb8XYZ8cxCLb68fcPE8BKTMYdRwANNebQSk/jvg9aA7pk/ggu5vDaKNAoHSNpflGLxNltPTZPhbwwlfQqaeglH6M6lMCtMa6DTkqiWlBIpPHcEH8Wa9GJVAy0SRogteLsrDqjXoDtKE0i6nsMop8G6ZUD9do+XEim0eaFyx/tgOdXQbF5gGKfJthNM0jnpEzNUytsWR73RitFgEoIGUWxUlMtneWojaoAYmPqGz3kTSPXJ6aBepllyVjnvaVehlK5pDiBTCMfhktuVkg2d4o6p7eVKUO2d0NyFFJTAuUTU1TkEODzgYLAG0ozaBqONHh1VFEUVIbixiJAlLk2zhmNJwaAl5wHvngRQ4z/XQbbCoC0DxvBiUo0h7xIeTT12BB+RxAtjeCUZ1EgIKBZsgLInoKEh/m1liyvV7MZBE3B31gqVmgYazILqO9jX4Uu8+OBeD36pPRAug8ZYadJs5SZHd3IUclMS0wE/UGaDNvhl2mSqPkw+v+FA9eoMi3HnoTpeLPAd2fpyigccyUA8qRb5pvjGJNGSzZXS+0xrqHOZ1E2e5DSWoWqJc9YxkIogSfh0WnTokPgA6wZlCrRVr0zzcsy1Ag1gTFSh3KInYSo2XfCmR74+wyVRlYPEtRs0DnIUclMS0wUwILFCPfg0ma4PViZnFtDhUj38MU+daFYveuxiC8Hv1TdfEAS2NeL0a7HysUG1yQ7fWQzOYxWJgnzGjG0TyvHzPOeaBkjaUxrxsztm8tNDISRAkjaWoWqIedJdUirM4GC0Bxnh8i57xuimusOafNIO0rdTGa5jGaNi+jRXbXj9k1lmxvnKLtjeuy5gRRlcchnIMclcS0wEyDBYCyy4wiSZIp/SwPy6gHKbK9Prot2siT3fVjNjBC2WXGUPQpG4McGk1JfFDkWy/dqi4rrbFOIkt8GM/28HlZNBX+VijTRh87TdgdoIxKM5iRVwHI9kZRKqTawj5E/EYkPsjuRjHTmBQgOSejCKKE3cqe3sD+Jsh5EPVTQ163IEclMS0wO8GTrooxhpI5JHNyQxC94s8KtLExhuIgNqJbBpDdzWBGwwkg2xvFrHO+reAsy+ZFJLIU+daD+QwnWmON0JfIIpsX4WEZzDUg8QFQSaBRitqgVIrpNGowkPaVjmLeQSyP+fGsgFSO1lg9mJG1AUordSgYqIe9Y2nkRQmch8GsmNk1lmzvNOSoJKYFRSFc2tQ4iVJ+bFT8GSDbG6VYck/OMifJ5gXsixcaLBiOfNOmxghmS6NCPi8ifnmeItvrw3xZGo15Iyh2n9sQMCTxAVAGt1HMS3yQ3Y2Q5gX0J+R9Ca2xzmJWJzHq98Lvlecpcpjpw7pKHdrT60EZ8/Mag/AYkPgASFbITchRSUwLdpoQwgXoEGUUM7oeCmR7YyglOgtbzGV7UORbHz2jGUgSEORY1YZ6oewyY+xUy4/NzDe0mdeLJEkWrLFkdyOYzbIByPZGsUozbiTNI5enZoFaUfY2sYBXlS3QC415Y5it1JGbBdKeXi/yGmtNRiWNeX2YkS9ToDHvHuSoJOqesTSvisQb18+iTY0RzHaCBUhXxShmy48p8m2M0s0kw5iLvtKY18cuSzaUZHu97E/xaqk8SXw4i1mdRICyy4xi1jnfFOTUDJ0hanChme5hK9bYQhlsguyuh10W7OlprtfPwHgOmbwIlpEbZBqhPUoZ3EYoSnzQOXY6Qo5Kou5RhOZbQhyiAf3izwDQHpU3NftTPHiBIt9aUQ5RRh3EAEWijDBB/Jki345iNssGoDFvFLNaxADZ3gjKmO+M+hHkjEp8FDogJ3MQRGpkpJWdJnUSAXIcGIEXROwZM1cGy7IM2sKU5aQXsyWwAI15o5jVBgXI9kZQ5vk5DQH4vGYlPrLULFAHu0zKHQAk8+Em5Kgk6h6z4s8A0Bz0QZGmoMi3doqOA/MTPJXBamdfPKOKP882KP4M0IbSCGr0lTbyjmM2ixigyLcRrHAQtxYcNqIE7E/RGqsVK0u/aY3VTs9oGqIEBLwsOgqZSkaguV4/qnOe5A4cRZIkS85TFAzUjyV7m8IaywsSxjIk56QVK5MPaI11HnJUEnWPFfoSLMvQAdYApJ/lDordzYg/A2R7I+wyWQ4ITNzIU+RbG4lMHsMpReKDNHGdxGwJLAB4PSxaQrLeHNleO9Zkl9GY10upHq7R8mOAbG+EXRbIHZDd9TOa5hEvOLjMrLF0ltKPFc6yAOdBrFBVSLbXjhUSZupZKkF2dxpyVBJ1jxXRV4A2NnqRJInKYF3CCrsDKClLI9trxQrbKxv5nCCqBwOiOoo+ZVOQQ4PBBgsAZZcZQW2wYOLwCtABVi+iKKnjnoKBzmLVGku2148VlTptYdnuKV5AMktrrBaUoEh7xIeQz5iMlvx5WmP1YkWjQIDmG73kBRE9YxkAZit16CzlFuSoJOqeXRZkHAA0wetlcDyHNC+CYeTMPqOQ3fXTPWxeGxQgx4ERdqp6NsZtH+Q8iPop8q0HK5qKABT5NoIVpd8AdSXVS28iA16Q4GUZzGkwIfERpUOUXqwa83SA1Y8VZbARvweBgtYf2V4bVmSWAZR8YAQrGgUCZHu97BnLQBAl+DwsOkniY1pCjkqi7rFiUwPQRKMXxe5zYsbFn4Gi3ZM5inxrpZjtYdY5T7oqesjwAvbFC9FXi2xPG0ptdFuQZQOQ3Y2w0wKxeYAE5/WijPmuxiC8HvNr7GiaRy5PzQK1YIWsDUD7Sr0ks3kMFnTizTiJ5WaBZHs9WKFPCdCYN4LltqdArCZ2lmjOsxbIaA2lqFmg05Cjkqh7rIp8t1IZrC6sKo2K+D3wFxydg9TISBO7LD9Ekd21sHtUdtiEOI86XxiFsln1YZXEh1ISSHbXhlUSHwCNeb1Y5SBuDHCqlvFgkmyvBSu0QYFiRiUFA7WhVEg1BLxoNCHxAVAGt16UMW++UofOUnqQJT7MV+oAdI7Vi1XzfEuIA8MAkgQM0znWUchRSdQ1o2le7W5mPtuDNjV6sOoQJUe+yfZ6sK4Mluyuh9LSKDMNFgCyvV52WaYZJ9udIt/aGE7lkMwJAKzQz6IxrwdVG9TkmGdZhvSIdWJ9RiXZXQtWNMdUINvroxgAt05Gi5oF1qZ/PItsXgTLAHMbjUt8AJTNqherEp3kZoG0v3EDclQSdY0yybSFfQj7jYs/AyUC0Ana1GjBqiwbgDaUesgLInpGLdJljZLd9WBV9BUozS4j22vBis7TQDHjgCLf2lDsPjsWgN/rMXWvYpMFsrsWrBrzAB1g9ZDLi9hbkPiwTBOX7K4JNQBOY95xVCdxizVNGvOihNE0b/q5DnR2lkh8cCYkPgCSc9KLFc3qFEhWyB3IUUnUNVbpegC0qdGLVZEogGyvh71jGeRFCZyHwayoddFXinzXptui8mOAssv00m1Rtocc+ZZLCsn2tbFKGxSgMa8XK7ofK5DttbN7NA1JAoIcq5axGqXU7rTG1qa7RDPOLG005jUjSZJlev8BzoNYQGkWSE6bWlhld4CSPvRCPoTpDzkqibpGzeqzYFNDG3l9WFUaBZDt9aCWAzaFTIk/AxT51ssuGzY1lF1Wm7E0j5HC+LTGSUybea1YO8+T3fWw09JsD6XJAtm+FqVj3rzEh2z3NC+qEgpEZayS+ABojdXD/hSP8aw8PudZcp4ip41WrKzUIbvrwyoJM4Bs7xbkqCTqGmWSMavhBNAhSg+SVCL+TGWwjmLlwkqRb32oG0ra1DiKMte0hDhEA+YkPgCyvR7UBgt0iHIUQZSwe8S6MljKLtOOlbI2YZ8HQU4+SpHta2NpYCRKY14rypjvjPoR5MxJfACUfKAHqxoFAmR3PfCCiD1jdqyxdJZyEnJUEnXNLhuEt1O8gGQ2b/p+BzL9iSwyBfHnrkbrDrCkq1IbKw9RADkP9KA20zGp4QTQhlIPVjZYAMj2eti138o1VrZ7PJNHNk/ZZdXYFy9KfMyOmZP4ACi7TA9W6iTKzQIpEKsVqxoFApR8oIedFs7zANleD3acY4dTPPKCaPp+BzJ7RjMQJSDgZdFZ0Os3A52l3MF86gJB2MhYOo/DO6M4uNX8BB/xexDwssjkRQwmc6ab8xzI7BnL4PDOKLwsY1r8GSgVgKZNTS2Gkzkc3hnFYR1RS+7XHvHh7aEkBqmxSFXSvIDWsA95UbK0FJPGfG36E/J8c8SsmCX3UzK4aczXZjTN4/DOKJa0hk3fqzHIwcsyyIsSBsdzmGtBkOtAZe9oGod3RhH0svCYlPgASuabJB2iarE/Ja+xh7abH/OAbPtdI2kKxNZgPJtXHQZWaFTSmNdOfyJr8RpLe3qtKOfYJRacY1vCPjBMoVlgikeHBQ64A5U9Y/IaG/F5TEt8ANTIyC3IU0PULclcHn+6dCUGxnOYHfMjmcsj7DM+ZJXI9+7RNAbGs5ZlrB1oJHPyovroqpXoiJi3O0CRKK0kc3ncdtZh6B/PYlbUYtsnyPaVSOby8LIMHl21Ut2MmEWx+1AqB0GULHFGHIgkc3l8ckUXTju4HZ1Wj3mab6qSzOXx+H+9u7DGBixbY/fFMxgYz5KjsgLJXB7L5zRYvMZSWZoWkrk81pz9LovXWLJ9LZK5PDiP1Wts0VkmipJpTe8DlWQuj4tXzsO/HdpBa6zDJLN5rPvc8ZatsR6WQWvIh8FkDgPjWXJUViCZy+PYrkabzrE0zzsJOSqJuiTDC7j9qR2485lujKZ5NAY5XHXiQlx3ymIETOirtEd8BUclTTTlsNPuAE3w1bDL9qSrUh277N4S4oqR72QO7bShnILd8w1le1TGTtvLjkqyfTnsszs5Dmph3xpLtq+GbXYPy3bPixJGMzyaQ9Y4QA8k7J5vKLusMhlewO1P22N7xVFJTIXW2AMLclQSdUcyl8ftT+3ATU9sU18bTfO4sfDf15y8yHBkhCaayjhld0mSLEnDP5CgMe8Odtrd62HREvJhqLChJEflRGwd81Ea89Wg+cYdaI11Dxrz7mCn3X1eFo1BDqNpHgPjWXJUTsLeMU8B8GrYbfvX+8n25aAxf+BBzXSIuoNjWdz5THfZaz98phsca3zY0oayMnbaXcnqy4sSRtO84fscqDgx5inyPRU77Q7QxqYa9o55sns1yPbu4ITd07yIZI4aGU3GCdtTBvdUHFtjE2T7ydBZyj1stT0FYivixJhPZPPI8LTGOgU5Kom6YzTDV3RmjaZ5jGWMO7qoDLYydtrd7/WgISBHscj2U7HT9uQ4qIyddgdoM18Ne8c82b0a9q6xZPtK2Gn3sN+LUKGsjWw/FZpv3IHWWPegMe8etMa6g512jwW88BWay1KjRucgRyVRdzQGODQGufLXghwaAuWvaYGyyypjp90B2thUw4kxT3afCo1593BizMczFPkuB62x7mD/fENBqUrYO+bJ7pWgMe8eToz54RSPvCAavs+BCs037mCn3eVmgYrtaX/jFOSoJOoOXhRx1YkLy1676sSF4EXji2J7lCaZSthpdwA0wVfB1jFPzrKK2D3mKYO7MnbaviHgBeeRNfoo8j0Ve+cbGvOVsH+Npbm+ErTGuoP9ayzZvhJ22r455IPSZH2I1tgpODHfUDBwKo6tsQmyvVNQMx2i7gj7vLjulMUQJQk/enanTV27aGGdjGJ3QNbysNLuANm+GortJUi48xlrx3xbeGLk2+uh+JSCc2OeNjWTsXPMy5FvP/aOZTAwnkVXY9DCJ5/+2LnGkuOgMjTfuIed843qOEjmIIoSWJYaGSnYPeaV/Q2N+anYOeY9LIPWsA8D4zkMjOfQGQtY+OTTH3vPsRQMrITt8w3Z3nHIUUnUJQHOg5MWteDLpyxGIpNHc8gHXhRpI28zAc6Dz7x7Pq45eRGGkjnMigYssTtAB9haBDgPzjq0A9eevBhj6Txaw9aM+ZawDwwDSJIc+aYN5UQCnAdffP9BuObkRRgcz2FWLIC8RWO+2GSBxnw5ApwHFy6fg2tPXoz9KR4dEb9l8017xCc7KinyXZYA58H7DrJjjSXHQTUCnAeXvXuePWtslA5R1QhwHnxwqfVrbGvBWSaIEkbSPFrC1H26lADnwdUnFdfY2THrxnwxu4zGfDkCnAcXFNbYkRSPdkvXWH/BUUlzfTkCnAfvXdgsr7HZPJqDdI51ggDnwaXH2bPGku2dh1JriLrlP365CQtXr8NomofPyyLsM+9XL+3OKIqS6fsdiKzvGcHC1evwtb+8aZndAYoCauFbj7+FhavXYf3uEcts72EZtIbI9tXYn+SxcPU6nPfgS/B5GAvHPGUR1+JnG/dg4ep1+MWmPRbPN2T7WnziFxuxcPU6jOcEC9fYot0lidbYcjy3U15jv/nYWzaNeTpEVeLrj23FwtXrsGnPqGW293lZNBV00cj25Rkcz2Hh6nW44GcbwdmyxpLdK/HA+h4sXL0Ov355L803DvOxn8lrbIYXbVhjye6V+Ff3fixcvQ43/m2bpWOeMiqdZ9o5Ku+66y4sWLAAgUAAxx13HNavX1/xva+//jrOO+88LFiwAAzD4I477nDuQQlT5AURg8kchpI5NIWsi063heUJPi9KGDXZbfBApTeexVAyh2TO2iYUpKtSm96EbHsrIn+l0MamOordh5M5MIx1ZXtk99r0FeYbWOzTIttXJ5sXsD/FYyiZQ3PIXEOLUpRSzGxeRCKbt+y+BxK98QyGkjmkLG70VBqIJcqj2D7os3qNpUziavQmZLvvT1u9xpLda9FXsL3VUPJBdZLZPBLZPIaSObRYeI5V7D6eFZDK0RpbDmWez+StbfRE51jnmVaOyt/+9re4+uqr8c1vfhObNm3CsmXLcMYZZ2BgYKDs+1OpFA466CDcdttt6OzsdPhpCTP0j2chSQDLFMtqrMDnZdEQkCMrtLEpT288AwCWlwi30YayJqrto35L70ub+erYZXeKvtamN2GP7VvDZPtq9MXlucDnKWaDWUHY70W44AQi25entyBHYPkaGybnfC16C+O+M2r1/oYyuKtRtLvVexuyey0U28+yeL5ppWBgVZR5PsR5EPFbFxiJ+r3wFbTmKShVHmXMd9g239CYd4pp5aj83ve+h8suuwwXX3wxDjvsMPzkJz9BKBTC2rVry77/2GOPxXe+8x184hOfgN9v7WAl7KUvUZxkPBYLkxe7dtEEXw7F9rMs3sjThrI6ShYxYP2Gsj1Ktq+GOuZj9mxqEtk80hZnTx0oFG1vz3xDke/yKHbvjPotzXACaDNfi37FcUABKUfJ5gWMpOVKGuvnerJ9NWyb5wu6rCNpHjmLs6cOFPoKwUD7xjztK8tRancr11i5WSDZvhr9iu1tW2PJ7k4xbRyVuVwOGzduxGmnnaa+xrIsTjvtNDz//POWfU82m0U8Hp/wj3AeNQJosbMMoA1lLfri9jptyO7lkTXdit0UrYQaGVXHrizihoAXnEfeoJLDrDyK7clx4Cy9Nh1eAbJ9LYq2p2Cgk9iVRQxQRmUt7Fpjm4M+KLkMdpQ3HwjYdZ6iYGB17MpkBeg8VYtemwPgZHfnmDaOyqGhIQiCgI6Ojgmvd3R0oK+vz7LvufXWW9HQ0KD+6+rqsuzehHbsKsUEaDNfC7tKMZXD6/4UD16gyPdkFLu3R3w2ZBFTFLAavQl7ytLkyDfNN5UozSK2uhST7F4du0oxAbJ9Lewugx0cz1KzwDL0Uhaxa/TZNOZZlqFAbBUyfDGLuJOCgY5i7zmWbF8N+2W0qFmgU0wbR6VTfOUrX8HY2Jj6r6enx+1HmpH02aThBFB2WS3sigI2hyjyXQ17s4gp8l2Nflsj37ShrISiRexhGbUJi1WQ46A6dmU4AbTG1sKuLGIlE1+UgP1pWmMnY5fdgdJGRjTmy9FrUykmQGtsNZSzlN9rfRYxBaSqY5cWMUC2r4Vd51hlb5MTRMQz1MjICaaNo7K1tRUejwf9/f0TXu/v77e0UY7f70csFpvwj3AeJzaUtKmZiihK6B+3p/S7tKSZbD8Vu3SEABrztbD3EEUbykqoWsQRP1gbs4gp8j0Vu7SIAcrgrgYviBhKFbSILba9r8QZQRrcU7FLJxGgeb4WZHt3IC1i97BLixigYGA17NQiDnIeRP3UkNdJpo2j0ufzYcWKFVi3bp36miiKWLduHY4//ngXn4ywA3sPUUp2GW1qJjOUzEEQJTBM0U5WMt03lCKfhCTkIKQGIAk5iHzSsnur5YAzdCNvp21rQVpC7tBrkx4uUOy4TpHv8tilRQxQBnc1BkqyiK3WIgYoKFUNKsV0D3uTD2iNrYQTYz6ZE5DK0Ro7Gbu0iAHK4K6GnVrEAAVincbr9gPo4eqrr8anPvUpHHPMMVi5ciXuuOMOJJNJXHzxxQCAiy66CHPmzMGtt94KQG7A88Ybb6j/f+/evdi8eTMikQgWL17s2s9B1KZYlmbjISpJE/xklIW1NeQD57E+jiHbPjEtF1cxn8HYS2sQ33wXxOwoWH8jYsuvRMOx14L1mt+IOKHLWq9j3m7bVkMoySK2w/Zt6oaSNjWTsXPMh3xeRPwejGcFDCZzaLBhwzqdsUuLGCg9RNGYn4zinLcjixiQ5/q3BpOq9itRpKhRSQFwJ7FTixgoWWNpzE/BziBs1O+F38simxcxOJ7D/OZp5VKwHSd0oGnMT8VOLWJAtv2O4RQFRhxiWs0qF1xwAQYHB3H99dejr68Py5cvx2OPPaY22Nm9ezdYtuhc2bdvH4466ij1v9esWYM1a9bgpJNOwtNPP+304xM6KGZU2hn5pgl+MnaW5wDT1/Yin8TYS2sw+uLq4mvZUYy+eDMAoOGYL4Llwqa+o9+BUszxrBz5DvnqZ+p3wrbVGC7JIu6wc0NJm5op2KlFDMi2H8/KG8rFrfaNoemIM6WYNOYnU7S79XMNQLavRr8DWcQjaR65vAifd9oUrNmOLL9hjxYxQGO+Gn02BqTkZoE+9IxmMDCew/zmkOXfMZ0hOSd3sNPuANneaerntKqRK6+8EldeeWXZa5OdjwsWLCBtqmmIJElUiukSdpZiAtNXV4VhOcQ331X2Wnzzj9C48jrT39Fr4wG2niPfTti2Gs5kEU+/Me8EajmgDYcoQLb9OxT5noIoSsXACJViOkpxzNvjnG+bpsFAJ7CzFLMpyMHDMhBECUPJHGY32FsJMJ1Q7G5fFnEho5J0WafQa3vygb/gqKS5vpTSLGJbkg+i9S/n5BZ2+g+A0nMs2d4JKORH1B0jaR45QQRgU8p8VN7U7E/x4AvfQ8jYWYoJTOOMyuwoxOxolWtjpr+jqOFk/eKqRL6B+rO9E7atht2bmnq1ez1AGdzuMJTMIW+rFrFP/R5BpGBxKWrpN2VUOo6dgVi2JFuQbD8RuwPgNOYr02f7np5sX45+27WIi3anhKyJ2H+OpTHvJOSoJOoORQi3McghwHksv39z0AclqDtE+h4T6HWgFBOYfmWwrL8RrL+xyrUGU/cvzSKeaYur3batRWlXTDuoV7vXA3ZvKKdrBrfd2J1FrBzMRAnYn6I1thQ1q8/m+Wa6rbF2Y7cWMUBzfSXIceAeVgRiqzU6pGBgeezWIlaCIrwgYYyaBU7ATi1igDS4nYYclUTdYfdGnmUZOsBWoN+BUkxg+tldEnnEll9R9lps+ZWQRN7U/UdtziIG6tf2sm3Ly3lYYdta2NmNFJi4kafI90Qoo9Id7La718OiJSQ3LyLbT6SfxrwrTNAitiGLGCDbV8J+LWKyeyXMauIqjQ533zNX/Tf20nch5uV9E52lymO3FnGA8yAWkCWcyPYTsVOLGKBgoNOQo5KoO+wuxQRoY1MJpyJR083uLBdG7Kir0Ljyq2r2H+tvRONxX5c7U5ts9qKM+SabsoiB+rU9y4XRcOy1ttm2FnZnESsb+ZwgIk6Rb5UJWsSUXeYodpdiAvUbGHEbyi5zByUA3hb2wWtDFjFAtq+EE1rEAJDiBSSztMYqmM0iFvkkxjZ8G6MvrlbleZRGh2MbbofIJ2mNrYDdWsQAzTeVsFOLGCi1e32dpQ5U6qejAkEUsHsjDygTTYIm+EnYn102PRfW7MDLGPzrp9B04s2Y95ke5JO98ATbIAlZsF7zi2GvjZ0ZFVrD9Wt7xuOHv3Mluo69BkJ6EJ5gOwDJEtvWwm4NpyDnQdTvRSKbx8B4Fg1BzpbvmW6UahHb0W0dKJlvEvU35t3EiTW2LeLDmwP1Od+4ie2auNH6DEi5TVFaxb41hRoZlafP5gB42OdBwMsikxcxMJ7DQj8dbYGiRrDRLGItjQ67GuXfKY35iditRQzIyQdvDyXJ9pOwW0arjbp+OwplVBJ1R7FMhLI9nMTubutA0e7J3PSKfI+9tAb8yFYkt/0ejMeP4b9/Hj1rlyA38LIl93cyi7geI9/5xG70/9+56Fm7BP2PfhS9vz/d9kxKBbVEx9bINx1gJzOTs4jdxu4sYoCyDsohSVLJfGOvc340zSOXp2aBCnYHYQHaV1bCbtvLzQLJ9pNR7G40i1hLo8M5DYqjkuxeit0SZgAFYstRmkVsd8LNUIqaBToBOSqJusMJxwFFvqcynhWQ4gUA9kWiIn458g0Ag9OkkRE/ugPJ7Q8DABqP+RIAgPFwENNDyA2/Ycl32K1nA9S34yA3+AoAgGE84IdfQ25gI6S8M5svOsCao5rQfjX6EmR3t7Bbixgg7bJylGYR2xWIbQxw8BaaNwwmyfYKzgSkqAy2HHZr4gKlQSmyvYJZu2tpdBjzKzqJ9bevdBO7tYgBOseWwwkt4pYQB4YBJEn+PsJeyFFJ1B3kOHAHJQIY8XsQsal0ZjpGvsc2fh+QRAQXnAlf25EAAK7lMAAAb5GjsliKOTM38rkh2VEZmHcqGF8MkETwY2/b/r1OZBED9W17M9QS2q+GM3anyHc5eslx4AqlWcR+rz1ZxHKzQDrATqbXkUodsvtknNAiBuo7EOsWZiU+tDSRbA4V53lqFliEzrHu4IQWsdwskPY3TkGOSqLusFvPBigpg03QpkbBCfFnYHpt5vPJPoy/8SAAoOGYa9TXfc2yo9LqjEpbdVmj9buRVzIq/W1Hwtd0CACA3/+W7d/rRBYxUJpdVn+2N4oWof1qOKGTSJHv8jinAw0MHkBj3ixOHF4BOsCWw24tYoDsXg4ntIgBsn05ek1mEctNJP+7aqNDJSiSFyWMpnlLnvtAwAlNXMrgnooTdgem1zl2ukOOSqLucGIz31bHjUUA4+WUZnCiEywwvUoC4y/fCUnIwj/r3QjMOVF93VeSUWlFFNnp6Gu9Rb5zQ68CAHytR4Jrlh2Vuf1bbf9eJ7KIgQNTfLuW0D7DVm8a1OtAQMrrYdFcaF50INneDJRF7B52dyNVaAuT7SdTXGOdKMWsvzXWLZzQIgYOzDXWLGpAyuC+Mjf0Gnp/+374Oo5G16U7MXfVNnRdthsNx3xRbXTo93oQC1D5dylOaBEDpfM82V2BgoEHHuSoJOqKNC9gLCM3WZmphygz5ZRmcEJHCKhv25ciZscQf+V/AQANx3wJDMOo17imQwDGAzE3BiG5z/R3OWF7ZVNTb5FvkU8iP7oDAOBrOxJc81IAAD9if0alc1nEB152mRah/Wr0O6DLClBJ4GRKs4ipFNNZ+hwogQVKmyyQ7RWc1IFO8yKSOcG275lOOKFFDByYa6xZzOokxl+5B/zIVoy/+QuMbfoB+h/9KEaev3FKo0Ny2kzECS1igOxeDie0iAGyvZOQo5KoKxTHQcDLoiFgX4ZTe8SP1rBPzaysF8yWU5pBsb2d5TmAnFHZGvbBU+L4q0fir94LKRcH17wUoYM+NOEa4/WDa1wEwJrybyeigAFOjny3hn0YTddPx/Xc0GsAJHhCnfCE2mUnMJwp/XYqi1iZb8I++zJKnEaL0H41nMhwAoq2z+bJcQAUs/qifi/CNmYRK3ZvDVfPrJ1JKFnEHTYfotoiPrSGfWDre4l1jIk6ifbZPuzzIMixaA37MJImhxngjBYxALRH5TEfOoDWWLOYkfgQcwmMv/kLAEDsyM/CG+4EP/wa+EL1SynthfkmQ2ssgOKYbw7Zp0UMFO3eEqI1VsEJLWKguMYyoEXWbuzbpRKEAUq1+hgbHVltYR+6v3YqBsZzyOVF8KKIsM/9P4da5ZSNK6+z7budikR9+tgufPMDB2N/iq8r25ci5rMYf0PepDWs+BIYZmpMh2s5DPzINrmhzvzTDX9XaRax3boqD110DE5Y0IR4Jl83tlf0KdVGRYXSb37kLUiSWNb2VuGEHi4AnLiwGd1fOxVDdTbfmEHkk4gtuxyj62+Zck0R2mc8voqfd0InEQBuPWspjpgVw2i6fucbJ3HK7p1Rf12usW7S51BZ2qXHzcPN/7a0rtdYJ0lk845oETMMg//36WNx4sJmJOpojXUTp+abkw5qkdfYJM03Cr0msojH3/wVJH4cXNPBCHSdjMyepwEA+fg7U977nQ8dhiNnxzCapjEPODfm5zQEaI2dhBNaxADwX++ej9vOOpTWWAcgqxJ1hRNZNhlewHf/+Q7ufKYbo2kejUEOV524ENedsthWDR0taCmn9ITabPluJ0p0MryA327eizuf2Vl3tgdk5wvDchAzw5h94bPI7H0Gwa6Ty77X13IYUm8/YjqjUikHtDuLOMML+Nc7w7jg5xvryvZKx29f6xEAAK5hEcB6IfFJCON74Y122fbdZjWctJDhBdz34q66HfNGkCQJI89dj6Z3Xw8AiG+5G2J2FKy/EbFll8tC+97qc7gTpZgZXsBf3hzAB+9bf8DY3ixO2f17dbrGuokTEh8ZXsDvtuw7oOYbsyh2tzuLOMMLeLZ7Py78xSayfQEngoEZXsD963fTmC9BkqQSqQl9tpckCfFXfgIAiB75X2AYBt7YQgAAH981IYCc4QX8desAzrqf1lgFp+b5O/7VTWvsJJyy/UOv0BrrFOSoJOoKu0sxk7k8bn9qB256Ypv62miax42F/77m5EWuRkWUcspyzkot5ZRmKHZLs9v229XX6sn2ijZofPNdRafL8isQmHtS2eR+rrnYUMcMpQ0W7MoiVmx/85P1Z3u1kU6b7KhkPBy4hkXgR94Cv/8tWx2VdmcR1/uYN8r4Gz9D4pX/RWbvs+j86F/ReNxXIWZHwXBhpHc9CSmfBqo4KidoEdts+3oc825idylmva+xbmJ3ps2BOt+YxQlpFZpvymN3YITGfHnMZBFn9z0Lfvh1MN4gIof+JwDAG50LsF5AyEEY3wdvdC6N+QoUtc9n5jnWTeye62m+cR7SqCTqCrujrxzL4s5nuste++Ez3eBYd/8kJJFHbPmVZa8p5ZR2YXc2az3bvrI26OqK2qBK5+/c/jdNdfh04hBVr7aXJHFCx28Fpzp/251FXK92NwMf34Xhf1wNAIgsvRDecAcYjw+eUDv6H/0IBv50PpLb/1/VeyhjPsixasdQqzkQbW8FdjvLyO6VMVOKqQWyfXmc0Kck25dnJu8r3UQZ80ayiONb5GzK8NIL4Qk0AgAY1gtvdD4AgB+T7U22L09RJ5HGvJM4oUVMtncesihRVyjZZXaVYo5m+Iodj0fTPMYy7nZDZrkwGo69Fo0rv6o2qmD9jWg87utyOeWkbntWkcuLGE7JP7tdh6h6tn0tbVCGnSpWzTUdLJco5+IQxvcY/m67M1mB+rV9Pr4TUi4BeHxqEx0AjnX+ttv29Wp3o0iSiKG/XQopl4B/1vFoWHH1hOvBeacCAJLbH656n6Ld7csiPtBsbxV2BwPJ7uVJ5fKI25xFTLYvj937SoBsXwm7AyNk9/IYDYDnk31Ivv0HAEDsyP+acM3bsEB+T8FRSbYvj906iWT38jihRUy2dx5yVBJ1hVFNFa00Bjg0Bst3SGsMcmgIuN89jfUGEOh6P7ou7cbcVdvRdWk3Qks+UlPzzQz947LdOQ+D5mDlBhhmqGfba9EGnQzj8YFrXALAXOdvJzSc6tX2aiOd5sPAeIrP4FTnb7uzPerV7noQ+SQkIQchNQBJyCF21H/D17YcbWfcD4adqMcTPvg8AECm5ykI6aGK93RCD/dAsL0d2G17snt5lHnezixisn15+hwIBpLty2N36TfZvTxGZW3GX38AEHn4O4+Dv/2oCde4gk5lPi47Ksn25bFbJ5HsXh4ntIjJ9s5DjkqirrC7DJYXRVx14sKy1646cSF4UbTle/Uy+Nin0LN2CZJv/Q49a5dg4I/nmyovroVi946IHyxrT4ZTPdte0QatfK28NqhS/m1Gp7LXAadNvdq+qE955ITXfUpGpY2l305kEder3bWi6Lbuvmcudt8zFz33zkOufxNmnb8OXOPiKe/nGhfD17YckAQk33604n2dKMWc7ra3C7s1Ksnu5XEii5hsXx4nGiyQ7afihBYx2b08RrKIJTGP+Kv3AQCiy/5rynVvQ6GhTiGjkmxfHqs0KicHiRUJKrJ7eZyQ0SLbOw8pfhJ1RTG7zJ6JJuzz4rpT5AP2D+u0W5ok8BCSfQAkhA8+D6Prb0U+PYTsvmcRmHOiLd9p9+EVqG/bK9qgoy/ePOWaog3KeKZmmnIthwHbHzaXURm3P6NSsb0E1FWXQDWjstDxW0HJqBRSfRAyo6pOkpUoWcRe1r4s4noe87UQ+STGXlqD0RdXF1/LjmJ0/S0Aw6LhmC+WlaIIH/wx5AY3I7n994gdcUnZeyuHqA4bM5yms+3txO5STLJ7eZw4RJHty2P3mAfqd411E8XuAa99WcQ05stjRNYmvfdfkPJpsIEWhJd8bMr1yaXfZPvyFDUqjc835Zt7XomGY69F2Bcgu5fBCRktGvPOQ45Kom4QRAkD4/Y7zAKcB9ecvAjXnbIYfYks2iN+SJDqZoLJJ/cBkACPD97YAoQP/hjGX/8pEm/8zEZHpf2HKKBo+2tPXoyB8Sw6o34Ikvu2V7RBIYmIb7l7ysagUtm9JRmVDtr+8hMW4NqTF2FoPIdZsQB4UXTV9pUyKll/DJ7wbAjJfeBHtsEza6Xl3116eLUrixgojvkvn7IY/YksOqJ+iHUw5mtRS7e1ceV1Za+Fl5yHkWe/jkzP0xBSg/CE2qa8pxgYcWa+uebkRRgsjPm8y2PeTZzIIgbqf411g16DpZh6qdc11k2cCMQCsu0/d/x8eY1N5jAr6v4a6yaldrcrixiYvmusnfTpkLUR+SQYloOvYRG6Vm0HP/J22T0v13AQAFlbXCHAefCl99Maq2CFFnHFIHEhkaLhmC8iwIVpjZ1EsTrN4TU25ocgzmzb2wmVfhN1w8B4FqIEsAzQHrH3ABv2efHcrv04Z+16fOj+FxH21Y/PXhjfCwDwhueAYVhED7sIgNygolz3aStQMlntzHBSCPu8uOLhV3DO2vX49ct768b2DMvB37kSXZd2Y95lPZj3mT1y1lgVbVDOgs7fTpSlKTAAFq5eh7PXrgfLwFXbi9k48mPvAJjY8VtB6fxtV/m3U4dXQLbz3c/uxDlr1+Nrf3mzbsZ8NYzotgIA17gIvvaj5PLvHeXLv/sdHPNhnxcn3PkMzlm7HlsHEtPC9nZRqkXcErIni1gh7PPin+8M45y16/HhB9bPaLsDRW1Qp9bYzzy0BeesXY/fv7KPbJ+wphRTC6Ikr7HnPrABXpaZ0bZ3QotYIezz4gf/fAfnrF2Pbz62dUbbHdBenVYq79KzdjF67luI1I5HIeYzU96rlH4LyV6IfEp9PeL3YuUd/8Q5a9fj7aHkjLa9FVrEWpt7hn1e/P3tIZyzdj0+9uCGGW13wBktYoWwz4tVv30Z56xdjz+82jfjbW8n5Kgk6gZlgm+P+OGxMcNJoSnow2t9Cbw1OG77d+khn5A7SHuicwAA/tnvgbfhIEi5BFJvP2LLd6qRKJuzPRT8nAev9SWwezTtyPdpITf0Kvr/71zs+flRYIMtYDy+ml3WuYbFAMtB4seRT+yu+R2TNWeEXBLNIXnT4cQhqiXsw3Aqh9f6EhhK5mz/vmrkhl8DAHgic+AJtky5Xuz8bY+j0slDFABE/PKY796fqv3mOsCobisgZ1UClbt/W6XhpBUvy+C1vgT2jU09fM0kSrOI7cxwUmgMcnitL4FtdbbGuoFTWcQKfo4trLEze8xPzCK2f3/TGvZhKJnDln1xjFToDjtTcEKLuJSI31tYY+tnX+kWWgKxIp/E2IZvY/TF1WpQUsncG9tw+5TECNbfBMYXAzAxqxIAPKw83yhrzEzFiixiPUFidY0dsieJZTrhZNIHAPi98p5+Tx2dYw9EyFFJ1A1O6AiV0h6RM0oGx3MQRfsa1ehFGJcdld7IXAAAwzCIHPpJAEDijZ/b8p19Dh+iFNsPjLvrLCsls/cZAICv6RAwrLboGOPhwDUdDKB2+ffkxiS775mL+Mbv4tnLj8VhHRG02ZxFDAAelkFrqD5snxsslH2XyaYE5N8DYF/nb2VD6USGE1DMEnfb7lpRdFvLoei2VkJxVCrl35Mpajg5s6Gcbra3i14H9HBLKbW7nc3gpgN6SjGtoGj7rCPfV68oASnOw6hBQTvxeVk0FbrCznTbO6FFXAqN+SK9GrKItWbuqe9nGHANSufvnROuke1lrDjH6gkSk92LOO1DaKvDc+yBCDkqibrByVJMAGgLy5NZXpQwmqmfyHc+USj9jsxRX1MclZmep8DHd1n+nU5HopTFdbCOFtfM3n8BgG4dUEWnslpDnWqRa37Ld/Gjsxc5kkUM1M/GJjdUaKTTdkTZ62rpt00ZlU5nEdeL3bWi6LY2rvyqumlm/Y1oPO7rsm5rlWzjauXfgiipf/dOZVRON9vbhdNZxG1heSOfzYtIZPOOfGe9oq6xLgRiZzKlJbBOZBEDpYHYmT7fuBUAn9l2z+YF7NeQRWxE3mVy52+Fekw+cIM+C7SIRSGL2LLLy16bHCRW7D6eFZDK0RoLzOxz7IEIOSqJukFxHJjplKYHn5dFYx1GvvPjSun3XPU1rmEBAnPfD0DC+Ju/tPw7nc9mrS/HgSRJakZlYO57dX2W09BQp1rkOrnlLrxnUYeu7zRDvWzmK3X8VlBLv0ffgSRYv/mlLOLaMB4f/LPeja5Lu9F12W5Nuq0KStfQyeXfpVrETmQRAxT5VnA6ozLs9yLskwXmZ7ztXcsiro811i2cLj8GKINboc/h5AOyu4yiAV0ri9iIvIs3VsionOSobKP5BoD5c6wkSRh94WbEll9RJkj8tSlB4qjfC79XduXM9KCU05JCtMY6Azkqibqhz5UNZeEAm6ifCV5ITCz9Vogc9p8AgPE3fm5pGZ0oSi5EourLccCPbIWYHgTjCcDfvkLXZ9WMyv1VMiprRK5z6fLX7KA96v5mXpJEVaNycsdvBU94NhguAkgC+LEdlj+DW9HXRDaPNC848p1myQ1uQf+j52DPz5bDE9Cm26oQPlgp/35qQvm3spnsiDqjRQxQ5Fuh1+GMSoA28wCQF0T156dDlLOoh1ca847juOMgKu8rR9I8cnnRke+sR3rj2rKIZXmXK8peqyTvopZ+U0ZlWcxWBo5t+Dbim76P3oc/gMgRl2LeZ/ag65Id6Lq0G+FDLpwSJGYYhmwP57WIARrzTkGOSqJuUMrSnMrqA+pzQ5lXun5HJzoqw0s+CoaLID+2A9l9z1n2fcOpHPIFjc4OhzKc6s3umT1yNqV/1rvBePXZgGtWMirfhCSV3xzXilz7g+Wv2UE9RL7zozsg8UkwngC4xiVl38MwjK2dv53OIo4FvPB5lMh3fYz7WqR3/g0A4G87AoxHn74b13BQofxbnFD+3etgZ0aFeptv3MLJrpgK9ZLB7SayRqezWcR0iJJRMlk7HAyAUwa3jNNZxM1BH5TYl9vNAt1Eq6yNlM9WyNyrLO+iln7HJzsqKRgIFLWIjayxyXf+gpHnrgcAxJZdDi46F4zHh+Tbj6Bn7RKMbfxu2c/R/sZ5LWKA7O4U5Kgk6ganu2ICRQ2tetlQSgIPIdkLYGpGJcuF1SYViTeta6qjZJa1hDj4vM5MCcpGfn+KBy+4H/k2qk8JyHp88Pgg5VNTBMYVqjUmCS+7Au8MTdUCsot6OMDmhuRGOlzru6o2LvIp5d8WN9RxI4uYYZhpd4BN75IdlcH5HzD0eaX8O73zCfU1KzSc9FJv87xbOD3mASrHBIqHqPaItixikU9CEnIQUgOQhNyU7rtaUDS4B8ezddUs0Gmc1mUF6iMY6DZuaBGzLEO2R0lAqsaYH3vpdvT+7hQE5r4P8z6zB/M+s7emvIs3tgCAnFFZWtnVRgEpAPq1iJW5Pp/sQ7DrJLSf/Xs0nXADYkdepr7HG5sPMT2EbN+LZe9R3N/MXNu7q0VMzQLthByVRN3Q63BXTKD+NpSyk1ICWA5sqG3KdaX8O7ntIYh8ypLvdMPuzaH6iXzL+pQFR6VOfUoAYFhvsUN1BZ1Klguj4ej/mRK5jq38GrhlX8RLvc7ZoB4i32ojnQodvxU4xa4j1joq3cgiBqZXdpmYjSPT+zwAIDj/dEP3CC/9BNrPfhhtZ65VnS4nzQ9iaXvEsSwbgCLfCsVSzJm7xrqBniCsmM9g7KU12H3PXPXf2EvfhZjP6PrO1sLhVZSA/ekZ7CR2WCcRKG1kNHPHvBtaxMD0WmPtQs1krTLP58f3Ib75bvAjWyEJWTAeHzyhtpryLt7YfAAMJD4JMV2UdKGAlEyvjmBg6Vzfc+889Ny3ELn+TYgd/YUJ7/N3rgQgV22J2fiU+5Dt3dEiVua1nCAinpnZjYzshByVRF0gSdKEiIhT1NumRi37jswBw0z98wzMORGBrlPRdsZPwTCsqYwLBTcyWT0sox6k3LZ9Pt4NYXwvwHLwdx5n6B5cjc7fkiRh4LFPw9dxNLou7cbcVdvQddkubO+4FMfftUEVw3aCehjzaiOdCvqUCkrpd87i0m83soiB6bWhTO95GhDz8DYulrOGDeAJtiHXvxE99y1UnS5N79yN5684Fkd0Rq194CpQ5HtiFrFTDeuA+sjgdhutpZgin8TYhm9j9MXVqqaxmB3F6Is3Y2zD7brWeZ+XRZPSLLCONLidxmmJD2B6zfN2odhdaxaxVZDttemyjq6/DZKQgX/W8QguOFPzvVlvAJ7IHAAAP7ZTfZ2CgRO1iGvNNxXn+vW3TJnrveFOeKPzAUjIDmycci8KBpbM8w7ubYKcB1G/XBE2k21vN+SoJOqCsUwemYL4tRtlafXSLS2f6AEwseN3KQzDouNDv0GufyN23zvPVMaFgtZDlNXUy4ZS1afsOAYsFzJ0D18NR2Wu/yWku/+Ewb98EmObfoj+Rz+K4X9cg3//zZvYOjA+40oxc4Ny6Xeljt8KXEnpt5UOJjeyiIHptZlX9CmNZlOqG/H1t0zYiMfXrwa/5bs499CYVY9aE6UsbSZHvt3LInY/g9ttVF3WGvMNw3KIb76r7LX45h+BYfVpb9VDUMptel2QmiC7uxMAB6bXGmsXtbKI+bGdSLx2PwCg6YQbdZfKehsWAADyY++oryljPpkTkMzOzDVWjxax3rne33ksACDbu37K+ymDW1sWsR1QINZ+yFFJ1AWK46Ah4EWQ8zj2vfW2qRFKMirLIfJJjG26Y8rh30jGhUKfKjY/MzeUZvQpFYoNdco7KhOvPQAACC35CLjGg8APvwZ+/5uu6PW5bXchM4p8YhcADY7KhkUA44HEj0NI7rPsGdw6RE0XHSdJklR9ypBBR2W1jXhyy12Y2+RcRmXI50XEL68r9W57u1Dmmtawz+Es4sKYT8xMuwPQXC0iZkfVdb38NX1axvUQlHITUZTQn3B+rp/pdgemahGb1V3V+vnppgNtB7Uak46+uBoQeQS6TkGw6yTd9+dihc7fJQ11on6vWhk0OEMbGSl274jWziLWO9f7Z8nl3+V0Kmm+cUeLGHD/PDUTIEclURe4IfIP1F/kO1/DUWl1xgXgjoYTUD+2Lzoq9etTKigZlfz+rZBEYcI1kU9hfNtvAQDRd12sNkni4z1qFrEbpZjJnIBUzvnINz/8GgDAE50HT6Cp6nsZjw9c40Hy5yws/3bDQQzUXwZ3JfKj2+XGUCyHwNz3G7pHrY24oNPpYpaZvpnXUg5oBzPd7kCxE2wt27P+RlXDuPy1Bl3fO9MPUROyiF0o/R5N88jl3W8W6AZKpc5RcxpM667q+fxMH/NA9Szi3P63MF5oxtl0wo2G7q92/i4p/WYYpm729G6hRydR71yvyFJl+zZMqS6a6XYH6Bx7IFO53eokjjrqKM3p4Zs2bTL8QMTMxA0dIaDEcVAnEcBi6XdX2etaonCeMk14quHWAbatDpw2+cQeuXyFYRGYfYLh+3gbDgLjCUASMsjHu8E1LlavJbc/DCmXgLfhIATmvk/9HQvJfWAgIhbwOZpFrES+s3kRg+M5zG/WvAxYgqJP6W+rnk2pwDUtBT+yHfz+txCcd6olz9BbEvl2kulSBpvaJXfpDsx+D1hfxNA9lI14ufmK9TfCG2g08YT6aY/48c5wCoPJ+ra9Xajlx66tsTPT7oD2BguSyCO2/EqMvnjzlGux5VdCEnkwHp/m71Wyy+plf+M0yphvDfvAeZzLy2gKcvCwDARRwlAyh9kNzh6e6wHF9hctb1G1+BSUKiAAcofpKs1bRD6JsZfWaP684jgYmqGBkVpZxGOb7gAkEaGDzkKgkKWnF67gqMyPdU94vT3iR89opu4DsXbRWyOTtRR5rr9iwrhWKDfX+9qXA6wXQqof+cRucLH56rX2aH2dY93ALR9CGwVibUfzCfXcc8+18TGImU6vW9GQqLwQ7E/x4AXR0c1sOWqVftc6/OvNuAC0l6VZTT1oe2T2yvqUvrblYP3GNfMY1gOu+RDkBrcgN/zGBEfl+Os/BQBEDrsIDMPCG54NgAEj5tDMxtEWK69HahdK5LtnNIOB8RzmNxvT5TRKVmPHbwWu+RDgnT8iZ2Hnb/ejr/W9qUkXHJXBBR8wfI9qTpfwsisgCjxYHU4Xs0wX29uFe1nEBcdBMgdBlBxtrFEv9Gk8RLFcGA3HfAmQRMS33A0xOwrW34jY8ivRcOy1YL36fnczPbvMrXJAlmXQFvahL5HFwHh2Rjoq+xMZtIZ9OKgthj2PVK4Calx5HQDZIcmwnDrmJZEHy4VrVhEpn1eY6WO+NItYsYViWyGzHy0nrUFo4b+Ba1xi+Du8VRyVwMy1fbFZnYaMSi6M2FFXAZKkaa5nvUH4Wo9EbmATsr0vTnRUlthdkiTdmqMHAm5XSc3UMe8Emh2V3/zmN+18DmKGoycSZSXNQR9YBhAl+SDltONiMvnEHgCAt0JGpdUZF0BJMx2XGou4mV2mOCrNlH0rcC2HITe4RdapXHQOAIAf2S6XljMsooddBABgPBw84U4IyV7M8gyjLWqso7IZlMi3G4ur2vG7hj6lAtekNNSxrvTb/TLY+t3USPksMj1PAzDeSAcoOF2OvRaAfKBUNuLhZVfAt+yL8PoqZ9HYwUzvjKmusQ7P861heT0SJWB/KlezycCBhiRJuhq6JF5/EL6Oo9F16U4I6UF4gq0Q+aRuJyVQH2usm5QrxazkELOa9ohfdVTORHrjWXRG/chnakiAZIbB+mIYe2kN4pvvmuiwOeZLEDPDuqqI5jcF0Rr2zWi7A0UtYqVsfoJtl12O4HzjQUjVUTneA0ngwXhkyamZHgzUo30u5jPoe/gMNL7765j3mR6I2ThYf4M8H1WY6/2dK2VHZd8GRA75uPp6W2GN5QUJY5k8GoP6JcCmM6IolcjHuZNwM1OziJ2ANCqJuqDfJY1KlmXq5gArCTyEZC8AwFMpo7Jw+G887uuqvgnrb0TjcV+To3A6N9zj2TzGs7Km4kyMRGX2/hOAuUY6Cr7mqZ2/E288CAAIzv8AvCWd3BWdylmeIVec427ZXhIF8MOvAwB8bVozKoudv63C7ewyuTukdV3MrSSz71lI+RQ8oU7NWa+VYL0BNBzzRcz7TA/mrtqGrku7sSl2Ef7j169b9LTamemHqD6XGkh5PSxaQvLBaSbafjTNI1vQKaxle0mSkNhyFwb++DGkuv+MkedvQM/aJUh3P2bou2f6mO+dlFFpVitRDzPe9vEM+hJZcIGmqlp8DBfB2IbvYPTF1VMaRMZf/hE8wbbqn/dFIOazarOdwxqy2PO1k3DPR4xnDE5nSse8yCfVsvsJtl1/i+HmmwDgCXWC8QQASUQ+sVt9vV7OUm6hahFr2Fdmep5GbnAzhp/6H4D1wRNqA+PxVT3DFRvqTOz8HeA8iAXkvLOZaHu3tIiB+jjHHugYclQKgoA1a9Zg5cqV6OzsRHNz84R/BKEXt/SzgPrZUAqpPgASwHLwhNorvq94+N+Drst2o+vSbvjaV0DiU7q/U8ksC/s8iAac1SpcoEa+3bG7kBpQs/QCc95j+n6c0lCn4KiUxDzG35BFy6Pv+vSE93qisiN6lmfY8YUVcG/M82Pd8DYsgifSBW+DtkxSrulgALKmp5iNW/Icxewyd/RscoKIeMb5RkZaULp9B+efbkkJEcuFAdaHwb9+Cj1rl+B/fvknpHih9gcthrLL9FctmO3UqzCTN/PK3qYxyCFQQ4s4N7gZ/Mg2MJ4AQgvOhDfUDjE9NOVgqpWZbHegaPuOaKCy0+bFm005bSoxk22vZBEPJXNI57KILb+y7PsajrkWrMeH+Ja7y14f27gGYqGKqByxZZcjs+95SPy46oDec+9c9N4/D8tHf2qLA7reUcb80raILc03AVk+yNuwAMDE8u8Zv8aqpd+119hU958BAKGFH9S8zwp0yo7K3MDLkISJe/eZPN+4pUUMzGy7O4Wh3+gNN9yA733ve7jgggswNjaGq6++Gh/96EfBsiy+9a1vWfyIxEzArVJMoH4mGrXsOzIHDFP9T5PlwmA8PnhCHej/v/Mw8MePYnTDt3V/pxv6lGrku1GOfP/vuYtrf8gGMvueBQBwLe+CJ9hq+n5q5++RtyCJAtI7H4eQ7AUbbEXooA9NeK83Ipf2d3qHXcmobA07P+ZFPgkuOhcd5zyMuZ96FZKg7RDhCTTCE+oEINvWLG5mEQc5D6L++o58p0oclVbBMAzAsBDTQziE2+VuFnGiPu1uN3qziK3MPmubwZ0x9ayxybd+CwAIHXQWWF8U/s5jAQDZvhcNfbeiwe12ENYtlEqdg1qCmp02Vjnn2+okAO4GY5m8mkXs5UJyFdDKr06qAvo6Ykf9N8TsWNXSbgh8hSqir6PhmC+BYb2Iv3znFAd0Yv1qXQ5oq37vbqPosi5pDWtqvmkUb8NBAAA+vlN9rV6SPtwil1f2lbUz51PdfwEgz/Va8TYuAetvgiRkkBt6dcK1mWx7t7SIgZk9zzuFoRSqX/7yl7j33ntx1lln4Vvf+hYuvPBCLFq0CEceeSReeOEFXHXVVVY/J3GA41YpJlBHjspx2VFZqey7HAzDoGHFF5Dp+TsSW36MhqM/D29ktubPO93EqJxezvJlV0DMf9mQBpcZMnv+BcAafUpA1u1hvEFI+TTyYzuQeP0BAEBk6X9M0Q1VysBneYbQ6opzXtFVsW7MV9P+KquTpKNBBNd8CIRUH3L7t6qHd6Moc40bWcSAbPtENo+B8RyWtNV+v5Pkx/eBH3oNAIPg/NMsvbev9XBk9z2LQ7jdyLs45mfqhnJyGWw19HbarUX7DO6Mqdq95uFVxPhbvwMAhA+5AADg7zwOAJAbek3WqdQp7aLYfTTNI5cX4fPOLLUnJQA+vzFU02kjZMbA+iOm1qlS6mVf6QaK3RuDHPycB3xiX0F3tRsSnyzuD7x+SEyNBpG+CBiPDw3HfBGNK6+DmB0ravlxYQRmHYeBP36s7HOUa7ZTDrP7k3pC2dP7OY8tzTcVuIaFSAPIj72jvjYTx3zpvve5K47DU9v7MK8xWPUzuaFXICR6wHiDCHS9X/N3MQwDf+dKpHc9jmzfevg7VqjXZnIgdn+KB+Cu/2A4lUNeEOF1uSHvgYghi/b19eGII+RGCJFIBGNjclTmQx/6EP785z9b93TEjCDDCxhJFyaaGRwRqdXxuxLB+R+Af/YJkIQMRtffpuuzWg9RVlCp9Epv5NsqMnsVR6V5fUoAYBgWXPOhAIB0z9NqxDR6+KenvFdxRnd6hl12zmsf89UyDqplX1lRcqfqVFqQUVnM3nbnAFLPm3ml27evY4UlWcal+FoOBwAc4t1VVwGpAyWTphoTsog1jHurSwZnsnaZ1mBgZu+zEMb3gPHFEFxwJgA5oOUJzwYkAdn+Tbq/uzHAwVvosj6YnIG2LxzaGwJe1WlTDlkrMYCx9daVhs/kMthiEyPZBqm3H8HAHz+Gwb9+Cp5Q+wQtPqlaaXehQSRQWkU0UcuvVkZmraxBpyUB7EbRSYwFvJptawRvbAGAyaXf9XGWcorJ+97e++dhxdiDmBWpXsqdekf2kwTnnQrWW92pOZlilv1EOZB6Occ6Rem+7WNHtOKvFx+Bo+fGHH+OlhAHhgEkCRhOGf97IipjyFE5d+5c9PbKTT8WLVqEv/1NLhXbsGED/P6Z1dGRMI+S4eT3sq50K6sXx0Gx4/fcGu+cCMMwaDrhRgBA4rX7wY/t1PxZVRvUAaeNXXo5RhAyo2r36cBcazIqAbn8mw22Irnt92D9jfB3roSv5V1T3qd0dZ/lGXZZl1XbmDfqiIxv+iEYxmv69841HQLAms7fburhAvptDzjnTFP0KUMWln0r+FoLjkput+PaoEBxnh9K5SAUhNedbK7hJqVaxBF/7Sxiq0sGjYz5AwVVG7TGmFfKvsOLPzIhi6tSAwUtyM0CZ9YBthTF9i1hX8Fpc0XZ9zUccw1YtrJWopH9yUxz2pSiakArjsp3/gQACMydGhSu3CDy65oaRNZyQNfKGqynfakV9JZUp6m2LVN2b6T5ZilK5++Jpd/Fs1S9Ngu0CjOJF+mCo3KyJJQW/J3l14N6Occ6weR92957u7Bi7EF845QFjj+L3CzQ+go1ooghR+VHPvIRrFu3DgDw3//93/jGN76BJUuW4KKLLsKqVassfUCifknm8sjlRQyMZ5HLi0jmjDWHKBX5t6J5g17UMtiEuxvKYul3bUflZNuL7ScgMO9UQOTV8jwt9E/aUNqJnXo5esn2PgdAgrdxMbzhWZo/V2vMNxzzJXSt2o7W0/9X/t8z1pa9jxCQNRc7PPvRGXXBOR/VnlFZ2xHpqbjRT3X/GWLOXMYDIJd+A0Bu/1vm5xsHs4jL0aYzm9UpZ5okCkjvltf14PwPqK9bNc9zBYf9LO8w5gScdwROiHwnc9Mik8Yq26vSKhoDUmYP/5MpZpdNH6eNVbbvVzUqK9teEngktz8MAIgUyr4VlPJvwzqV0+wAa5Xdx7N5JHPFLGKWCyN21H9X0Eq8ypJ1qpTpZnfAwvmmJItYyIyq1SuhheX1+EobRM77zF7M+8weWV5CQ9l1tazB6LLLpzQdmUw97EutsjswVROX8fjh71yJrku7Me+yHl22rQZXcFSWZlQqQZG8KGE0PT2yy4za3qiDO5/sQ7Z/AwAguPDfdD+vklHJj2yHkNmvvj4dM7iN2L6agzi9aY0r+7aZHJRyAkMCXbfdViwvveCCCzBv3jw8//zzWLJkCc4++2zLHo6oXzK8gNuf2oE7n+nGaJpHY5DDVScuxHWnLK7Z2XIyeg9RVlMvG0pBzaisXvpdyfZXH3s9MrvXYfzNX6DhmGvgKzh3quGkRqWdejl64fe/BTbYqkufstaYF/MZJLc9NEXnyFtG52hAbIEgsfAxeUSlEQDanaVWMDnyXS1AUG1Dlur+M6KHf7riRp8f3Q7W32D69y5FlwAAMvvfxqJv/QWRYND0fONEFnE59Mw3VmsFViM7uAWe8Gywvkb4Z8nOESvneU+gEX1CKzo9Q+gU3gHgbBMtJfI9lMxhOJVDW8hf9aChRdvMTqy0/eRSzFpIQg6xZZdjdP0tU64pJYOTdXer4UZGZTXN3FpYa/va2qDp3U9CzAyDDbVP0SwrZtBs0PW9CtMpm9UOu0f8chZxtu8lDD6+Ck0nrsa8z/RAzMaLWodatBINOue1rLH1gKW2TxSziNO7HgfEPLimQ8A1Lan4GeVv0xOShZu1zi9K1iAgz9vK33t02eVoWH4FRp6/Ac3vvQ0MO/VnyI/vc31faqXdganzTX70bfT/37nwhOega9U2MB5O19xdCW9MdlSKmf2qbqjf60FDwIuxjKzB3RQy/z12Ysb2WhzcylguJb3zMQCyvI6eJAkFT7AF3sbFyI++jWzfBoQWnAHAmLPMzBppFqO2r3YeSWz5EZqOc37f1h7x443+8Wmxxk5HLFH9PP7443H11VeTk3KGkMzlcevf38ZNT2xTo2ajaR43PrENt/39bd3RQPdLMevDUZlXNSq7Kr6nmu2/90YzfAs+CK7xYIjZUU2lonoaLJileuTbnF6OVpQS2vCS89C1ajsaj7lG0+dqjXk+O64rO6tvPI8BoVF+X+H37iRtYe2R72obslJHZPkP5yEJ5nSSkrk8bl+fRlIMwBdqwmlzcvCyjOH5pi/uXBZxOYqNjGpvKJ0qSxP5JHwth6LjnIcx5z83QRKySFk8z6d5AW/m5gEAGjPbLHluvSi2j2fydZFJUwmr19icIKA17NM85tO71yG2/ArLSgadbqZjJgvZ8v2NhkaB44Wy78jB54NhJ+YP+DuOBhgPhPG9qjyMHopNFuo728O+faVs9/iWu8GPbEVy+8NgPP4pWodW6/kpc02aF9XMznrFatv3ldg+ZaLMVSuTMzLnXNaD7Y0fRc9DH0D85R9gaN3nIEnihM/khl7Dvt+cKM91yy4ve1+zOo61sNruiczELGKgWCLsjXWB8VhXvcP6ImCDsiOOn6BTWR/nqVqYtb3RqgNFBqFSdrEWypV/67W7m7I3Zmxfe98mX3NSe3y6jPnpimFH5fbt23HPPffg5ptvxo033jjhH3Fgw7Es7nymu+y1Hz7TDY7VN6wm69k4TT2kbUtiHkJS1n31VMmorGX7pvfcilnnr0O6+6+aFqA+B53ElfRyoiu/huDRX7I9kle6MPesXYye+xZifOuvNC3M1ez+y0174PX6dDmU+hJZ9AktAIol/04S4DyIFTpe1xr31TZkWhyRYFhTGlQcy+LxbUNoO/thdK3ajt9dfCL2fO0k/PXiI/DYWwP65xuHO91PRldGpQPONOXvoufe+djzwCHouW8Bxl76LgJsHo+/NVD2M0bm+f5EFm/xsqPSG3/T9HMbQbH9vnjG8vJmK7FqjVU26+cd7MOer52Eb51aOQimIEkSRp7/FnofOhXhg8/HvM/0YO6qbei6tBux5VcaKhl0ciNvtqTf6v3N5FLMqc+bQmrHowCA8MEXTLnOcmFV39WITqUdjYzsOATaZfdZUT+E1ADGt8kd1Rsq6FRW1Epc+VU0HHuN7v1J2O9FqJAZVO8HWLtsPzvCqhlkdjoqgYnNdv7f60M4fu0u/Dh5AcCwyO57AbnBLcUxm88iH98Nlosg8eq9lX/vK75g677UertP1SLOFOYMxbllJWr5d7xcQ50De8wbCWyI+QzSu54EAIQOMu6oDJTJstddqeOi7I0Z29duihaBkBl11Ak7k3WgncCQo/Lee+/FoYceiuuvvx6///3v8Yc//EH998gjj1j8iES9MZrhK2ZhjaZ5jGX0RSDdLv1uCxcEv3kByaxxbZhS9G7khWQvIIkA64Un1FHxfbVsz0bnIr75Loyuv6XmAsQLIgaT8sTqVBfefKIHvo6j0XVpt3r43dTwn/jWkztt/V6zC3M1uwc5D4SMPodSbzyLXkHuqmwkS8YKtG5sam3IajsiQ6Y0qBLZPB6/5Chwg8+i576FGPzpQrW74uOXHIVxnX+zxfnGLY1K7Rv52s40c10Oa/1d3FDBsWVknu+NZ/AWPx8AkBt+3dRzG0XJJO4ZTcvj2qVMmlpYscaWBmb67u9C7/3z0LHrf2tu1tO7ngA//Dry43vgicwB4/Fj5LlvoWftEiS3/97Qz1OayZrh7c0uM5uFbOX+Js0L6r0qzTep7j9D4pPwxhaokguTKepUGnBUhrVncGvBrkwcq/eVxWqRABKv3Q8IOfg7jlU13soxeZ3qunQnfO1HIb75x7q+W2G6HGAtt32hamFB/lW5rDTQAv+sd5t+Tq00BOS/8cfTx6P9rN9i1vnrkHr70eKYvbcL2b71mHXB02j7t5+B5UITf++X7YKv/Sj0/d95EPmUbc9pvd2nnqWyqqOy8rg3SrnO33o1uN3CrO1ZLoyGFV+YknjRUCWwkdnzD0j5FDyROfC1LTf87Mo6ke1brzYtUub54RSPvCBW/CzgfgMpM7bnR9+uvG9bdjmE9ADim+5w1AlLGZX2YshRefPNN2P16tXo6+vD5s2b8fLLL6v/Nm3aZPUzEnVGY4Cr2J27McipmwSt9GnQcLKTiN+DgFf+U7BicTWykc8nCmXf4TlgmMp/ltVsv6glBD/n19y5UhH597IMWsPOaMlkdq/DwB8/hsHHVmHoic+hZ+0S3PCr32DzPnvLLM0uzNXsnuYFeAL6srN6Exk1o1JwIaMS0B75ljdkV1cpA9XmiCzNeCgtuatFA8cjv+W7GJvkfE+sX438lu8ixhk7RDnlnJ+M1jJYSZKQHdxSdVOW2vk3JN74ueEMp1p/Fycv6Sw7NxiZ53sTWbyVlzMq+aHXXekKWnqIEnOJ8uXNK78qdwF2SKupHGbX2EoO6LH1tTfrY5u+DwCIHr4KnkAjAMDXfCjE9JBaxqmXxiAHLytr9CnBMbswm4Vs5f5GqVjwe9mK91S7fR/y8Yo6hko2VMaAo9LKQ5SdmThW7ysVp83sKIv4K/cAAGLLy8+lpZSuU5l9z2HgT+dj5PnrwY/u0PX9wPQ5wFpu+8Lesm3k7wCA0MJ/K6sRaReldg/OPw3xLXdPDd6vvwXxl4t7v9LfOwAM/+OLyO55GmMbv2fbc1pv94mNAsV8BrnBVwDYk1Gpdv4u01BnJoz5kRdXy4kXl+1C+6oezL20G/72oxB/+c6y71dlEBZ+0JRmra/1CDAeP8TMfuRH3wYAtIR9UG45VGONdVv2xojtJUnC/me+ioE/fULetx33tSmVeQ0rr4M3PEvzGdgqinJO9T3mpyuGHJUjIyM4//zzrX4WYprAiyKuOnFh2WtXnbgQvFg9mjOZyVpCTsMwjGUbSqMbecVZVa3sG6hu+y+87yAIOhYgxe4dUT9Y1hmhd6X7o6/9SHhCbRDTQzjG96al0depTpsUxOyIqYW5mt3/4+i5yOdzuspA5IzKQum36xmVtW0//PQX1A2Z1Y7IWng8HMa3lHemjW+5Cx4dukulWcRua+IOp3JVI9+JV+/D0OOXlN2UNR73dcSO/jwSr96H0IIzMbbhO4YynGptWIXsWFk7GZvnM3iHnwMBHoi5MVcc9KXz/NjG76H3oVMRmHfKlAyq4X98wRVHqkLpfNMa9uHwzqjqMNZie6OBmezAZmR2rwMYz4T5TClTy/T83ZAzyso1thZmS/qt3N+UOg7KHUyFzChShdLYyd2+S/HPkp0Muf6NkARjWolW2N3OTByr95VKAPxo8VkI43vBhtoRXvIxXfcIzjsVwXmnA0IO+5/5iq7PAtOnDNZK2xeziCX4eh8HYK7M1QiK3SUYG7OsN4jmE+VGYmMvrbFtj2bXWUrJqMwNbAZEHmywTc1+tBKu4SAAQH5sp/qa03rERjFre0kSMf7GzzHwx48hO7AZ//77XTj39v8tBDZumBLYkCQJqW5Fr9Xc3wPj8cHXfhSAYsash2XQGtKWwe227E0t2+dFcco5Lte/CakdfwI/shWp7j+j4ZgvYd5n9mDOZXsw65LdeKN1FVhvAGJ2zHEn7HQZ89MVQ47K888/H3/729+sfhZimhD2eXHdKYvxjdOXqFGRxiCHr5++BNedshhhn75m8k42dKmEVRtKoxt5RafQG5lb9f6K7b9+2kTbf+P0g7Fq5Tx4dCxAfQ5rg0qShMzeZwAAgTnvVTtur/S/YdlGvlw2a3zzXaYXZsXu159+8MQxf5o85jl/RJcOY38iUyz9dimjUmvkmx/ZhvE3HsTAnz4OMT1suSOyFlZGf93IIp5MS4gDwwCSJJfplCPbvxHD//iC3PzhnT+pm7IJTmJ/I5rfd7tmqYdy1Pq7CISa8Mmj504Z8182MM/3JbLgwWGEWwBAbmbgNMo8n4wPIvHqveBHtkLiU6qDPT++B4N//STGX1uLxGtrHX8+hbDPi6+cshhvfend2PO1k/CvSw/Bnq+dhLe+dBy+osH2Rv9mxjbdIX//kvPANSxQX+da3gVvbAEkIYv07r8b+ZGKa2zCXqeN2ZL+Smvs9acfrHt/o+pTVpC1yex5GqwvBq7lXfC1HlHxPlzTwWB9DZDyaeSG9f3dWHmIqjmucuOGs7trrbFG5hsAOHz01wCA2BGXgvHq2+swDIPmk24HGBaptx9Buucfuj4/XcpgK+3pv2FgT6+ssUv9vRDjOwCPD8H5H7DluSuhyDm1hX26gvelhA8+H/7Z74GUT2H/s1+z5TmrjXmjaywgJx8ARSdWYNZKW7rOe1WNynfU15T5pt6zy8yO+dzAZojpQTBcBIGOFfhX9378eWQJhFmnAiKPkWe/PvH9Q69ASPSA8QYR6DrZ9PP7TehUVi2fXn6FZtkbs3N9uTX2K6csRoDNTznHpd75I2advw7tZ/0a0Xd9Wk2MeKoHmLv6H/jGE7sBuOOEnS6Z89MVfbNggcWLF+Mb3/gGXnjhBRxxxBHguInOl6uuusqShyPqlwDnwQXL5uDakxdjKJlDa9iHv28fgqgzC0UQJXUT51YpJqB/My/ySTAsJ+vv+BshiTwYbwhCZn/NTZFSWlKKWvodre6oBGTbn7iwGV8+pWj7N/vHEeA8EPkMYsuvxOiLN0/5nHJIYzzygVFLN1IryY/tkLU4PT74O49V7XCU7y2M7k9CkiRTmymRT2LspTUYfXF18bXsKEae/RqCC85EbPkVE64pTLZLJQKcB5ccNw/XnLxItfvf3hrE7tE0Dm6LqOXPjSuvg5gdA+tvgCTyZXUYe+NZSErpd8L5rt+A9jEff/V+AEBwwZmaxqfVKBuPcn9XejcepRt5p7KIJ+P1sGgJ+TCUzGFgPKseKhSE9H4M/OkTgJBDaNE5iL7r0+rfhfI3o4xVrmFh1TKXxpXXVX0WIT2M2LLLMbr+linXlL+L/zp+Pq44cQEGx3Noi8hj/vGtAzj3iFm6fm4l22M8dDBax3YgN/QaQgv/Tdc9zNJesPXy8d9BkpLwtR6J4IIz1Ou+poPRdMKN2P+vL2P/P69BcN6pExx2TsIxPFq670bvH+5S15mW5VeCa70WQPUySiN/M/lED5JKw5EVX5hwjWEYhA46C/HNdyHV/WeEF52t++dxKusgP74PsULDlPiWu1XbxZZdLmuHeYM17xHgPHjPgolr7NYBeY0Fyq//5QI3RZmJiX/jyuf97Ueja9X2CRlJ5WAYFv7OlUjvfgLZ3vXwF7JptKAEIgfGs6bX2GrjytdxLBguiLENcvBEtfvyK+VgnQY94gDnwaqVXVPW2L1jGSxq1RcY601ksJTbiYb4BoD1InrEZbo+r/5cLe9C9IjPIPHKT7D/H1/E7H9/UXMZ83Q6wAY4D844pH3Cnn7D7lF1zGtFGfPnNLwMAAjOOQmsL2r581bDV5Ba6EtkDe8fGIZBy0nfxb5fH4/k1l8js+xyBCpoyJohwHnw+fcuxDUnL5qwxq7bPogPHdap616TZbSyNjbSAYoalXx8FyRJBMOw0yaLGJBtf/6RsyeM+afeHoKWrWF6l5ysFew6GRnRo2ouNr3nFsQfPh7J7Q8js+8FBGbL2qzp7r/I7593iqY1qBZFOZAX1dfaIz683l/d9mObfoDEq/dj1vnrAIaZOFcvuxyxZZcjN/RazbGuJIWYmetPWNA0YY3dNpAEhwzGNkw9x42uvwVgGDQc86UJ99k6OI6hZE7NIlY09bWcga2iHhryHsgYyqi85557EIlE8I9//AM/+tGP8P3vf1/9d8cdd1j8iES98tOXerBw9Tr85uW9OPnu5/DhBzbg96/06rrHUDIHQZTAMMU/djdQo4BJDR3TympQroGYGQXrixmK5uTHewAAnkj10m+F83+2EQtXr8Nz3fuxcPU6fOCeF5DmhcqdK8tk9ikbyk6HMlmVbEp/x7FgvUFwTUvBBloRZHNY6t1RUVxZK9WyWYee/Cwajv2y4c7TCs8U7H3D49vw3//vVZz34Ev48XM71etay597SzMqk3shifY2mCiHMuaHqmxqxHwG4288CACIGTzomaVaM5+ozqYnlRwHTlPUtJE3NqWRacYbQPP7v4vA/A+g7QP3V3UsmMk2FfMZDK37bHmdxpK/i95EBgtXr8O//3Ij/vf5XTjvwZdwx7/eqXjfSiiHKCF2KADozgyzgvaID0Emg9PF/wcAaDj2min2jR11FfyzT4DEj2Poic9AkvSV31mBIiEyNllj8sWbMaohUzYf3607YyK++S5AzCMw9yT4O1ZMuR5aKJerpd/5iyGbqGPeZo3K0RdXo/ehUxFceOaUkv7xN36p6R6SJOGjD26YsMaeec8LyOUFXRrUqqxNSUZl6ed71i5Gz30Lkdz+cE2pBv8suRmGloY6pfPJvAYv/nrxEVjQHEIyZ26dkbNVy3fNbj3tJxjbcLtp/cqndwxj4ep1WP3kdnz296/gvAdfwk+e36X7WXvjWVwU/isAILzoXHg17q3K0XT89WD9jcgNvaKuh1qY3yT/3oemyQH22j+9gYWr1+GfO4ax6JZ1OOUnz2P74LiueygB8Pf75Eyv0CJ7u31Xoj0iBwNHkind3ZkV/B1HI3LYReCalkLKZyzvdK+wd0xeY//zV5tw1zM7cd6DL+EH/yrfFbkaRakJedwp2Xa2OSqjcwHWCwg5COP7AJRmVE6PMf+T53dh4ep1+H+v9uH4Hz6Dc9ZuwKOv99f8XEpxVM7/gKpFHPCyaJ6zHJHDLgIA7P/Xl1UJGUWfMrjQmr8H5XeaG3xFXTuUQKyyxk4pn96/FYlX7wc/shXj2x6aUqkTXHAGeh86FX1/+CCyA5srfrcVWsWSJOHDD0xcYz/5q401qhLvmlKVWFxj5Z9dzxnYKpQxn8jmkba5WeBMxJCjsru7u+K/d97Rf4AhpicDiWxBtJfBhw+XI39r1+/WdQ/FcdAW9sHrMTQcLWFyd8ZKKe2VJ+jViL/8AwjpQUNlZ4KOjMo0LyCRzWMomcOZS9sR4uRo3h9elZ3ErDeAhhVXq521532mp2yH5aI2qEOOyj3/BAAE5pwIQI5YB+bK//9Y/xumo1HVnDa5/g2Q+JThztMK/YUxn84L+Niy2QCAX2zcg2xe++IkiBL6E1kMCI0A4wHEPIRU7Y2R1WiJAqbe/n8QM/vhiXYhuOBMpx5tAsrGI7Zyqnh28Kgv6dp4KIcot/RwFZSNTYaf6vjouW8Bcv2b0PGh39bMFjVT5jL6wo1Id/8V/X/6OGJH/0/FvwtlzO9P8fj4stlgGeCf7+w3cICV53qu5XAAAO9K6bcfF4SfRAMTh7fhIISXnDflPQzrQdsH7oOvbTliR/23bYfTalTbrCdqaAHmE3sw8NhF5bVNV34VsWWXI73ziQmfEbNjiL96L4Cp2ZQKgTnvBcNFIKT6kOvX3zSxzYHsMn5sJ5Jv/Rb8yFYwrE8NGqW6/yJrhz33dYi5RM37jGcFpHkRQ8kczjqsA34vi+EUj+F4XNcBrXeSvIqZA16x8/eLFd8DlA+krhh7EM9fcSxG0madBwxiR00NbDS95xb4mg+xRL9SmW8yvICPHSmvsT/f2AO+RifbUnJ5EXxqGOeE5D2HkmFrFE+wFY3HfR1c01J4wrOrzgmle8fPrJyNv158BPxe9/a2elBsv7AlhBVzGwEAD2zo0XWP3ngGTWwcS/A6ACC48INWP6YmlDX2pX05U46LphNvwayP/x2Znqcs73Sv0D8u230kzeOC5bPBMMC67UPoHtbXcbzUaSOkBpCPdwNg4O84xpLnnAzDeuGNzgdQbKhTLovYaImwEwwUbO9lGZz9rg4Atc+xYjaObO8LAIDggtMnzPMMw6Dp+G+C8YaQ7X0eqR2PQEgOQORTYIOtCB1kzd+DNzYfbKgdEHlZixTFNVaSpLLrQHLrbzDr/HVoOmkNYss+NyWxwtd+FDyhdki5BIafvhpCerjs780KreKRNA9ekDCUzOGcd3XCwzLwsCxyaX39BJQAeOk5VktzTyuJBbzwFfwX9S55MB0xvXpKkuSq6DzhHv3jxTLKTx/bZegAq5Rizqqg4WQXkxfOzx7bjqXtEXAsUzljgk8CjKfyBL3lbnjDs9Cw8jrdm6L8eMFRWUOjEihqACmdRD99bBcAYO364oaS9UXQ+9Dp6H/0o8j0v1z2e/tVbVBnbF+qT6kQmC07Klf6zOtU1nTa+CKmG74oY7494scZh7RjTkMAwyke/6chAqswlMxBlACJ8cATlh38wrjz5d9aytLir8jOi+jhlzjatXMyrDeALU0XY9YluzF31TZ0XdqNLQ3/gQ/ct0nXAbZvUvTVLRTbH9bmKe+4WH9Lcc6pQrVs09iyylpDmd71ajfThqP/B55AY8W/i/6E7NzoiPgxtzGIMw5pBwCs1XmAVeb6hllHAgBy+7fqbgxilrYgg0sifwQAhJZ/AQxbXv2Ga1yMWec/iVz/RvTcO8+Ww2k1amXK5itkykqShKEnP4dc/0sYfurzaFjxRcy9rActn96N2Zf2ILTko+h96FQM/PXfJ+jtJd9+FIzHD655acWABOP1Izj/dABAqvtPun8mJ7TL4pvuACQBgXmnwd9xtPp6ePG54JqWQMzsR3zLj2veR5nnwz4PYgEOnzqmC61hH1qjIV0HtH61uYX8s5s54Pk75YxKfmQbhMxI2fdUcoQm1q8Gv+W7iLC8+j4jjoPktofQ+7tT4J/zngmHwNjyyy3TElbX2KgfHzy0HR1RPwbGc/jTG3rW2Cz+Z+FWhMMxcK3L4J99gubPViK27LOY9fGnkO19oaLDavLecc+9XVgx9iB+cPYS09/vBOqePuLHqpXyvvLBl3qqNn2bTF8ii/cHNoGFCF/rkeBi82151loogdgdwylTjguWCyH+snEdaC0oe/qOiB/zm0M4bYlcbfPABn2JH30lck5K5jXXfIitzVG8BWmUvOqolO0+nOKRF0RdGehuUGr7VcfOAwD8bdsgdo9UdhKne54CxDy8jYvBNRw05RzrjcxBw4r/Ade0FAwXBeOPoeOch9G16m2wvpglz80wDAKdK8EGW9XGPYrtTzsoUnFfGd98N2KHX1K2Uof1BtB+9u8RPOhsdHzot4i/fGeZc3AaQnrI9Fyv2L0xyCHs9+KiFXMnSDWUo1zwva+ChJldzT3LITcLpPJvuzDsqPzZz36GI444AsFgEMFgEEceeSR+/vOfW/lsZbnrrruwYMECBAIBHHfccVi/vnoZzEMPPYSlS5ciEAjgiCOOwF/+8hfbn3GmUJzgfZjTEMSZhQOsngisG6WY5RbOhnfuxvNXHIv/XN5cMeMh8fqDEDO1oz3FTVGP6lSJHf0/FTdFkpiHkJTLJjwaMipLF1aGYXDxsV1gGODvb0+MwLKBRvDDryE/sq3sfXonHaLsJB/fjXx8J8B4EJh9vPp6YK7stDzGvxUDCX3R48lUddroLBGuRH+JxqGHZfCpY+TN/AM6MomVMd8e8cMblT+fT+hz+lhBLUdlbug1ZPc9CzAeRA+/2MlHm0I2L+C0+7dg7up/YPDpa9Gzdgl++uhP8ezOEfz5Te0H2F6HnfOVaIv40Br2YW5j1FRkumKZy8qvIrb8cqR3PTnlM2I+g6G/XQJIIsKHfALhxR+u+h2lASkA6gH2ZzoOsEoWMQC0dy4B44sCIg9+tPzcZBeeXb/HbO8QBoRGJLs+XvF9Ip/E2Mbv23o4rUatzTpT4bAz/vpPkd71OBiPH43HfwOsL4Jfbh7AifdtxaqHXoev9XD4Wg8HxDz2P/M1CKlBSEIOga6T0LVqOzrOfhgMU3lbqHQrVcrY9KB3I6/XmSakBtQGSI3HXjPhGsN60bBS7to8tvH7EHPVg6n9k5pSXHxsFzqjfqSStTWoS5lcimnGmecJtsLbuBjAxAYKpVRzhCa33IVYKGjKcZB49T7wI1uRG3h50iEwVHvMciFk+jfV/L0OlOxvOA+Li1bIe6IH1mtbI0U+ic6wB1/5j1XymD7rV5Y0EpFEHvHNP6o4JwiZ0YpO4uzmNXWVRVaOVC6P8axcGdIR9ePswzrRGvahN57F428Nar5PbzyLUwIvAXC+23cpkzO4jTouGJZDfIs9ne4VJq+xFxccZg++1ANB1JYIlMuLhQo3eU+fsVmfUoGLKQ11ZEdlc8inajzyOfMlwnZTtL0Pi1rDeP+iFkgS8OBLlZtcKvqUoUKTqN5J2qAA0LDiGsz6+N+R3fsMeu6dhz0PHIKe+xZY6qRtOuEmdK3ajmDX+yAJOVy0vAXHz2/CIR2NVRJqppZPl+IJNKLtA/dWbNIYf/lOeALNphvWlPoPAGDVynkYSubw9PY+Xee4emjGC0wvPeLphiFH5fe+9z187nOfwwc/+EH87ne/w+9+9zuceeaZ+OxnP4vvf//7Vj+jym9/+1tcffXV+OY3v4lNmzZh2bJlOOOMMzAwMFD2/c899xwuvPBCXHLJJXj55Zdx7rnn4txzz8VrrzlfcnYgMrnD3MUGIrBOl2JWyjgYe/FmiK/fjcM6q0zwL98JT7BF0wQtb4r8GF733+hZu0QtEyiHkOwDJBFgvfAE22v+DJM3NfObQzh18dQILNdwEAC5iU05Jh+i7CSzT86m9LUfNUFY3dd6JDIIIcqmkB541dR3yE6ba6rq7Zml9BAFyAdYAHh82yB6RtOa7tFbUqqg6GblE853/p4c+Z5M4tX7AAChRWfDG9bXOMVqFOfGaJqHr2E+xPQQLuzcDgBY+6J2J2+5MhHA+dKk9ogfnVG/7jKXcpTLFilmzv0HMvuen/D+sZfWgB95C55QB1reX3u9Vh2MhTFfeoB9TOMBdnA8C1FCQYvYD1/LuwA42/lbkkTEX1oDAHggcTYGM5WdF1aUNpmhqi7rssuxfsfeKQfYfGIPhv8pC803nXADfM2yFmj3/hRe60sgyHnAMCxaz7gf4YPPR+eH/4D45rvkzK+1S9Bz30KMb/111UNUaMG/AWCQG9yiO7iiZyNvxJk2tvlHkIQM/B3HIjD3/VOuRw75BLyNiyFmhmtmVfYV1kZlnl/UGsailhCCoSZdB7TJwUCzHUmVrMpKOpW1HKHgExhbb8xxkBt8RS47Z72IHvapKderZ3dfjkzvi+CiXRjb8J2qv9cpTpvCvvIvW/sxkMhWnauVcdNz71zsfeBgTWNaK9XmhPGtv5Yz7ypdr+EcqAeUzPmAl0XU74XPy+KTBSexHkmnobE43heQG+mEDtLfdMsqrHIcaA0umNlDKJUeisbguYd3oinIoWc0gye3a1tjlb8bL8ugJeSzXZ9SQen8rZR+e1hGzj4P++Dn/K6uo1qYEpRaWUw+EMs4iSVJQnqXLJ0SXKA4KpV7lJylGFR09lnhpBXzGSS3/x499y1Ez/2LsPueuYhsvwtPXLIMYnrQ1L6S5SIVmzSObVwDUeQrymloTQqZPM8f0h7BexY04fN/egfBo7+kuSqxKGHmtpwTZVTahSFH5Z133okf//jH+Pa3v41zzjkH55xzDm6//Xbcfffd+OEPf2j1M6p873vfw2WXXYaLL74Yhx12GH7yk58gFAph7dq1Zd//gx/8AGeeeSauueYaHHroobjppptw9NFH40c/+pFtzzhTEEVJFexVJpqzD+tEm84DrNMNXaptNtPdf4aUi1ec4PNjOyDy+oS5PaE2iOkh1VFX9r6Fsl9PeLam8tq+Sc4yQI5GAcBPNxQjsJM3EKVIkqTexwmNysyeqWXfgKwHtzewHADADT0/+WO64Ue2w9dxNLou7bZFm2Syc740AvtTjZnExW7rfniUjEoXSr+bQz4oiSZDkxpciHwS42/KjSdiR3zG6UebQqmzLDTvVADAUuElABL+srUf+8a0HUTLZRG7UZrUHvGhL5EFF9Tn+KjEFK2h1sPBNR8CCDkMP/MVVWson+xDw4ovoP3s36P1zAfhCbbUvPfkMW/kANtb8vvzelj4CjqVTjoq0z1PQ8gMIymF8avkB9SgQzmsKmM1SlGX9atTMmUbll8B37MX46U3X55wOGYDzWg74wGED/4EYkd9Xr1XcczLcyDrDaLllB8ZOkR5Qm3wz5K7mKa6/6rrZ2rT2A3WiI6jmI0jseUnAMo3SALkrMpGJatyU/WsSlXuoGRtPO/I2Xh6ez8iFZrJxJZdPmH9F0RJ/VmVQ1Q+2WdIw1ohoOpUlndUVnOEehsWgfVFDWeHxV+7HwAQWnQOPOGOqd9drYnByi/DE2zTNOYmzzdL26N4z4ImHNwWQcwnVJyrK2U0WuYYqDInMN4ghBrVNoLNcwZg0lmmOOcLOnsAcElhX/nHN/rVNbgW84VX0dS2GGJsKXwl8gtO0xae2LDOKFqy28V82tQeYmB84p4+wHnwH8oa+6LGNTZe+vuTHHRULgBQLP0GioHYfMbddbQWGV7AWCYPoGj7846YhVjAi50jaTy1Y2jKZ/iRbXJ1mMeHwNyTAJTP6rMz2Fkt4Sb32o/hC7Wa2lfWDHgJvOnmpJXOsVsHxvGRn72C2NGfL+mzUP4cNzmL2E2c0OCeqRhyVPb29uKEE6Zqvpxwwgno7dXX9VkruVwOGzduxGmnnaa+xrIsTjvtNDz/fHkHx/PPPz/h/QBwxhlnVHw/AGSzWcTj8Qn/iKkMp3KqQ0yJXJYeYLWWwvZP0vawm2oTMD+6Hay/ofoEz4V0CXMrjrns3mcrPpNQyEzR0kgHKIkAlkzMSgR2z1gxAssVHJX5Mo7K/SlZyBhwxkmc2fsvAMVGOqWMROUskcZ4+XI2PeT6X8LAHz+GwccvsUWbpH98qnO31ElcLgI7GdVBHAuomqSCC6XfHpZBa6h8FDC57SGIuTF4GxYhMO8Ux59tMv0lTvXAnBMBlgOT7MFHF2QgSsDPNmqz32Q9Gyu6FxqhPeLHUDKHTT1DtsgVMAyLtjMeQOigc9D5oYdUraGee+eh576FyPVvQkCjbttAmYDGqkIm8Z80HmD7Jkl8+FoLDXUc6PytHN59jYvRtWo7Yh/+G+a2tVeNfNfWu7VGZ6oaQ2kGYy2nouvSbnRdurOgBXgl3vrDJ9HpGcGKhfPKNmFqPf0nEwJeqhZxqdg8F66YMVHrEGW0/LuY4ZSrqmtu5ICXeO0+iNlRcE0HI7TonIr3jiy9UM6qTA8h/spPKr5vcrYHAHz0iE58/cldCCz7n6lZ+yu/itjyK1RnHjA5i9gHfnQHBv58odzkyGDWv+J0yPatL2tDUchWdIRGjroKeQ3SNWWv8ali4OrwSys+X2UtwKDcbEfDmOsvN9+snIc7zjoIyY3fKTtXxzf9EKw3YGv2VrU5Qcqn4QmYDzqZcTSaDbiV29u8qzOK4+Y1Ii9K+MXG2lUfIp/Edy/5NDrOeRgL//NFSHltVSZ2YFVGZa1MYSHVazhLWaHsmC+ssY+83oehpIY1tiQAzo+8BSkXB+MNqWutXaiVW/Gd6mvtET/6Ell4AjFLArF2odjd55H1/gEg5PPiwqPkSqdychNKNmVg9nvU+VrVIi7J6rMz2FltjRzbuAaiYC7jUZPefzmJsxVf0JwUUjzHFt9//rLZiPg9ePytQax/83X0rF2Cwb9+uuI5bmBSFrGbaA3EEvox5KhcvHgxfve73015/be//S2WLLFHNHpoaAiCIKCjY2Ikt6OjA319fWU/09fXp+v9AHDrrbeioaFB/dfV1WX+4Q9AlEmmJcSBK+nWvUpnBNZpjcpqEzDEPIR8rqbjQI8wd2DOewAUDhb58vYoNtKZo+ln6C8TiQpwHvz70fLnlQist7CB4MfemXIPxe7NIQ5+r71NUoTUAPiRt+TnLNijlFyLnKEzO7PJdGOu3JDcadIbm2fqPuWQpGKGzOQDbCzgRff+FJ7eMVzzPqV6Nt5oofTbhYxKoPJmfvwteX6PHnFpVc06pyh1HLC+CAKFrK5L58lO+AfW99QcO6VZxFY0tzCDYvdvPrkbDUd/3ha5AtYXQevp/1s+i2n9LdoPUWXG/OGzYljZJR9gf67hANs7KSDFqaXfr+v6mfRSenjvWbsYPfctRLjvL3j+imORFyvLk9Q6nKZ2/Q3jW39jq2TA3ngGLz1yJXrWLkFu8JVCxmwr+ON/DO8J30di848qNGGaqIc3OaNSea/RQ5TiqMz0/F3Xz6tkOGXzIpK5fEXbCZlhXc8m5XNIvCHrozccc03V+UrOqrwOXNNS+FoOr/i7K7fGhnxerJgTw8Zff1zO2r9sl7r+hw/9JHofOhUj//gSEm/+AsDELGJWzGDgTxcg178Bw0//j+HGHr62I8F4/BAz+5EffXvK9cQr95Z1hDas/Do8S/4TjM9Y6Xly20OQcnF4Gw6qGbiqpAWoZcwJoqRmyJTa/vxls/H+JR0Yr5ANmur+M8Qq1TBWZG9VmxMiSy+UncQVroeXXYGR8eoa3GYcjVYE3JQxv6R14ppzcWFPv3b97qprrPL8e+9bUNDjm+9q05R2ixwHVTOFj/kSPMF2w0EfhXJr7PI5DTh6TgN4QcIvN9XeHxb3lQFke+Wgv6/j6IoN46xCqdwSkr0QeXmMt0d8WC48g8yuJ01lkNtNqT5laRa+0lTn4Vd7MZKaGNBU9CmDBX1KoHxGpVmZj2rUmkvT2YypjEetev8sFwZYH4ae+FxNibPJFBt3FR2MEb8XH18mn4me3PIKxPQQxFz5nxMoLfv2g2XN6xCbodgskEq/rcbQCfSGG27A9ddfjzPPPBM33XQTbrrpJpx55pm44YYbcOONN1r9jI7yla98BWNjY+q/nh7nM52mA+UWVmBiBFbLATabF3F4ZxRzG53JqKy62Vx2BV7YHUf0mGtqTvBahbm9jUvAhtohCVlk+18q+x5Fn9CjoeM3gLLOMqBYpqNEYJWMSjE9CDGXmPDeoVQOh3dGcWh7RNN3mkHp9s21Hg5PoHnKdV/7CmQkHyLSqOrQNEqukKGlaOBZyUi6mIXaXrK4lkZgtZTCprICDu+MYkFTCJ6IUvrtvEYlUPw5MnlBdRzkk33oOPshtJ/9MKLvcreJjsJkx4FyWD4CmxDxe7B9KIl/vbO/6j32p3jEAl4c3hlV/3asinrrdVgpdt+wawi9j34Yvo6jMe+y3ZbLFcilniYPUWWcNgCw6jhtB1gAGEvzOLwzisWtIQDFjMp8fOeUuckqKh3e44UOyO/pClb8bLXDaezozyPx6n0Izju1pt6eGfoTWSzw9kJMD8ETalNfX3bQQWhZ8B7Nv9esIM83cxuK48nMIYprPgze2AJIQhbp3es0/zxhvxdhnwdL2yMIsvmyThkhMwrWV6OqoaBxrP7NZYYw+xPPoOPcPyKy9MKazxFZ+u81uzdXWmM/N2cLusafwo5HL8JoIl6UWmhajNAiWY9v6MnPIrPvOcQzOXzk8E68u6sBQ+s+h9zQK2BD7Wh+77fB+qLGGnt4fPC1HwVgavl3dmAzRp79GnofOhWRd108wRGaPuRKvPuuDdi8Z9hQBreiVxw9fJXhwFXtMRfDcCoHUSo2G1OI+L0QMpUbGWmqhjGZvVXVYXXstfD4omWvN6z8KvzLvoANvZUPsFodjZXWGSsCboIIPHbxkVh7/rsm3P/fj5qNEOfBmwPjeGGXvm7zbjZNKc3gNkvF5ARfBBI/bkEH5KlSE0Ax8UPbGpvH4Z1RLGoJqXNDwOaybwBg/U1gfDGwwVYISbmi8t3Ms/hRyxqMPPNVxI7+fNlmfw3HfMnWTsxaqLS3OaarAUfMiiKbF/Grl4tOYjGfQWbPPwAAofmnq6+r59iSNdbO5p615lJfIIr+pGSi0331ua7098YwDLzRubLEmY79wECi0jlWPhO9uVMOxHnCsyveYziVxeGdUSx14BxbC2qmYx+GdhznnXceXnzxRbS2tuKRRx7BI488gtbWVqxfvx4f+chHrH5GAEBrays8Hg/6+yd2eO3v70dnZ2fZz3R2dup6PwD4/X7EYrEJ/4iplNOXUNC6uCZzeTx1+Ql4dNVKHDWnEclc3p6HLUGdgMtkMPmWfwmf+cM2vNybNTzBT4ZhGLXcWXHYTUYoOKl0l35Psn1pBPYXG/fKG/eArEFXWv6dzOXx7nlNeHTVSjz+meNtt3ux7Pu9Za+3xSLYnF1SeG9lLU8t5IblDC07yl0UuzcGp2ahKk7ichHYUpK5PO786BF4dNVK/OeKucj55YxvYXwfJFGw/Jlr0R71Y2l7BKcvihUzz9Ty4I1guJDjz1QOZb5RxOaDXbJOZX7fP/CJZfJ8XstJLIgSur92Kv5v1UowYJDM5S2JehvJhFE2NccyLyK37zkMr7sCYDyWyxWYdcRKklQxKPWJ5bMR5FhsHRjH8xUOsIA85j97wgI8umolbjvrMCRzeXiCLfAUGjSZzao0cnhPbrkLi9qq/24rHk79jWh+7222CuUDwNDIMNo88r25QrdnBSEzpun3mszl8cyVJ+LRVStx+KyYOtebOUQxDGOq/PuOsw7C2Eu3VyjhvQNCegCxKjqQ6X3PQciMFOer+xag576FyPa+AEmq3cRPErJVuzeLfHKKTiIASKKA5m1yM6b7Emfj129MzJBres9NCC36MLjYQRCycbxvQQy/OX8+Hr5oGcJLzgPXfBjaP/grzet8JfwFncpMiaNSEgUMrfscIAnwtb4LXMP8CY7QN4YFbB0Yx81P7yl/AF35VTSs+ELZeae0iU7ksIsMP3ftLOUnEH/7z3js4iPR89WTwGSH5L/n3Djir/0U/mBlRyTEPCTBPseAQq1qmsnXuy7dCX/7Udjw6wvxzuBoxftqcTRWW2f0ZiGX4+IV7Vgx9lPsva9rwv1DHgFXvXcBlrZHEPXyFeZab901TWmPyo7ukTSPXF5bc89qVEpOMLuHEMWSNXbSnv7Co2bD72Xxam8CL/VU/h0mc3n893sX4tFVK7H6g4ci3fsigGLzLTthGAYdZz+ErlXbwXAhSPkMLnn3AoSal+DlzHywvuiUvwlf+1EY+tulkET7z3zVqLS3YRhmwjlWIbvvWVnmITwLXOsRAIDxbB4vfv69eHTVShzSHlXXWD3OPr3USrh5ansf9oxlDXe6B2rPdaUE58sSe0pZvBYqnWPfPb8Jh7ZH0Ai5Oq1SI89kLo/3L2rFo6tW4pGLVzriP6iGVRncxFQM1/StWLECv/jFL7Bx40Zs3LgRv/jFL3DUUUdZ+WwT8Pl8WLFiBdatK3rsRVHEunXrcPzxx5f9zPHHHz/h/QDwxBNPVHw/oZ3JndJKuWC5HIHdOjCOl3pGy34+wwu4/akd6LrpSSy6ZR1m3fA3fOepHcjw9jtrWG8A/tknyNpfl+1WJ+DLH3kLWwfGsWskZWqCn0xgdnVHpd7S7+Ihaqomh9qxboPsJOYmlX8rdp994xNYdMs6zL3pCdvtntlTWZ8SkA+v63OHye8tODWNIKQGIKYGADDgCh1vraTonJ9q9xVzixHYP7xaXlpCsf3cm2Tbz77xCXx/fRJgvYAkQEhVlqSwi7aC46Cs9peO8mC7GRifOOb9nceA8UUhZvbjsoPlDfxDr+xDIlP+MJrhBdz1XDe6bnoSB92yDp2F+UYwebg1mkkSC3jh87D4ePhJAEDksP8E47H+MGf2EDWWySNbOORNnutjAQ7nHylHu3+3ZV/Zzytjfk5hvplzY3G+URvqmNCpLH94XwMhO4Z8srfq4T2XLn+tlHLrAMMw4BoXmc5UrUVyv9zVPsk0Tfk9+TR0n662xpo9RIUWyo7KdPdfKjoHyzmQT1ncgvcv6UCiklNjy93whmejYWWFsrUVXwADIL7pB2X+5lZrmq+0OIV4oTDmSw5R42/9GvzIW8h5GvDT8Q9NmecZhkXbmQ9i1sefRq73Bey+pwt77pUDP7n+TZh1wT8RnPu+qs+mhXKdv+Nb7kaufyNYXwOa3/+9KZ9RDlEv7h6d6ky7bBd87Ueh9+EzkR+f+neceE1uVhladA684cpB/lpUz1K+ColX7sXCg9+LY8YeQO/98yb8PYcPOgvZfS8gWqWMFAyLhmOvRcPKyRmN5h0Dk3+OanvD0utiNo5df7kEC8efQOeu8vMFoCGglEtUXGfim34Ij9+cPqbIJ5Ha9B3E15dfx65971w8f8WxmLX7J1OzoHNx5JN9ddc0pTnoA1uhWaCVmM2c25+eqvev0BTy4bwjZEfN77aUL/+evMYuuvFPapM6uxvpAIUsw55/yN2n752P3YX5ru38p/F4y01gWO+kv4lRDP3tUiS3/x5jL33H9uerxuQAeCn/cfQccB4GL++N49VeuVdFaqdS9n06GIZBhhfwnacrn2P1OPv0UG0u9S//Er7w525LHGZaz8HBLrnCKTf0KoRkf9n3TKZcMBCQncQXr5yHDo9cIeWJTHVUunGOrQWVftuHIfGKTZs2geM4HHGEHFF49NFH8cADD+Cwww7Dt771Lfh89oiaXn311fjUpz6FY445BitXrsQdd9yBZDKJiy+WyxMvuugizJkzB7feeisA4POf/zxOOukkfPe738VZZ52F3/zmN3jppZdwzz332PJ8M4lqjspYgMNV712A4+Y14/BZMQyMZ9EY4MCLIsI+L5K5PG5/agduemKb+pnRNI8bC/99zcmLEPbZp6vCx3eh/5EPgQ11oOvit8B4fGA8PqQLk5wVpSKlKLqMmd7nIYnClM7eeku/+yd1Ei3l34+ag3tf2I0bzjgEOUFEPjwfwAak9u8AXLC7kBlBbuhVAJUzKtujPmzIFhyVe/4JSZLKdm2thZJN6W1cBNaGTMCi4PlUuzMMgy+etAgNAQ6nH9ymeczf8OQOfHRBGyJiL/KJPZqd1VaxoCmI9y/pQO/9lQ/ujSuvc/SZyjHZ9gzrRXDuSUi98ycs4V/CWUvfh0vfPR+ch61i++3q/ZRx3xr24fJ3XwtIIuJb7oaYHQXrb0Rs+RXy4bbGhrKW06OS7RiGwbuiY3hfYDMA2FZirxyiRl+8eco15RDFeCqv14rdYwEvgtxULdsr3rMQHzliFk47uBUDiSwag9rn+c82HQbsfgK8wc7fIp/E2EtrMPri6uJrBYcV4wmi4eirwPobyx6gWX8jfMFGQ9+rfE+tg3lpubYRhLEdAICEv4xOtsgjsuxKxNeX/73m8znc+vTeGnO9fIhqXHkdxOwYWH+DqsFci8Dc94HxRSFJAvjhN+FrnSi1oTiQ45vvKvmbuhI/OOdL4NOVS3hLbVf22bgwAnPeg4E/f6Ls57XMV1p+dyzk9UdpbiEJPEZfkMdZbMUX8ctTTsIpS1rRl8igOehTx7wgiki8fCfG1t8y4Z6j62+BBBbRFVeD85srU/PPkjMq8+N7IeYzENNDGHnuegBA03tvLZuBUjxEZSGKknrg9ITa5J9tw23I9W/E0BP/hY5z/09df7U20dGKcnCf/HtlPAE0vecmxDffVdZ2ABA5fBXCnccDDIvE5h9NGFcNx16LjOjF/S/24OCmi3DyJdcikxpFINSIv2/rw9sv9OLilV227ivL4Y10YkPn1/C+fV/GkUP/i+zAp+Bvn5rQocgdlBuXarf2Ks0zoss/h9jyKybMhQpa5nmG5Srqf8Y3/whdK76A0Ze+i/j6yXPtzWA8gZpzrRtNU1iWQVvEj/5EFgPjWcxusEdeSnEaAbKtJo/LWvOpUvbdHOLg807NHbriPQtw/rLZmtfYucJWsBCQ8rYh4+uEnUWx6hpc5m9WAINPHjV1zvBGZqP55O9j6PFVGHnhZgQX/Bv87ctNPQPDcqrdlXVCC+WaGCm0hv244oQFOGlRKxa3hjEwnkW2xFGp9RxbOtcCqPp3qIdKc+nlj76NrQPjjmb2eUJt8LUtR25wM9I96xBZ+u9V3y/r/cvjvpztLz62Cz37AAwAKW8bInmxbvwHlShmVOYMn2GJ8hjKqPyv//ovbNsmD4p33nkHF1xwAUKhEB566CFce+21lj5gKRdccAHWrFmD66+/HsuXL8fmzZvx2GOPqQ1zdu/ePaHr+AknnIBf/epXuOeee7Bs2TL8/ve/xyOPPILDD7e3C9pMoJKGk8LXTj0YG/eMYs6NT6DzW39TM5hSOQEehsGdz0ztRA0AP3ymGxxrb/OOrKKZGJ03waHVZpPGhK/1SDC+GKRcHLmhVyZck8S8quuipSQswwuIZ+QU93K2bwr58NTlJ2DjnlHMuuEJ/HCzHKnd/NYr4FjWcbtn9z0HQALXtKRiNkZz0Ict/MHgJQ+E8b3Ix3cZ+i4liuxrOczo41alUpmIwvnLZmPjnlHMvWnimE/z1cf822l5Ey84oFM5OcvpwiOaNTkO3KZsA6l5cvl3uufv+OUnj8bGPaOYPWm+SebyYKvY/vrH30JmdLfcHOPSbvVfcOEHNTlszJRWfyz8NDyMiGTTu8E12dOEzmzmXKXyHIUjZkWxcc8oum56Ep03aB/zP3ymG/6CPEPOoKOyVufLapku4WVX4Ll3Bgx9L6BNb88s3nHZdtngwqnfwYXRtLJy9pjIBjXN9UYrBxiPD50f+TO6Vm0HG2icUAZaLcs4+8qPEAo3a8r+qtyURVvZeyW0ZBl3j8hl3cpcP/7mz5Ef2wE21I6G5ZdjQ4885mff8MSEMe/1+pCo4PBJbPkRvF7zh1RvdB46PvxHdH36TflwHmhG25kPInzIJxA9fFXZzyh6j6IkZ3CVwng4tH3gfjAeP9K7HlczKAEgue33EHNjmproaKVsljLrga95aeUs5S13wxvuhMT6Mbzgs5h1yW60fHo3Zl2yG0MLPoucxIFlGHzz8bfwbw+8irmr/4ET79uKuav/gQ/+9FVc//hbtu8rK5GcfS7+kjoeHggYfPziKU0VE2/8DKldT1RuOnL05yHU6NYOga/QPONrmub5ausYWC8YT+VxPbZxDUSRR6SCXENk2RUQBHeapjhVjqlmzl22u9ABeafmzLlaa+xRcxp0rbHLfXJA9oXkIvg89jbKrCWvcnRXa9lrkaX/gdCiDwMij6G/XVKx0WgtzHa6V3QSFzSV16u+8cyl6jl2+U2/Rn7/65DAALPe7/o5Fig/lyoVMFYn3NQiqOzHd9XWqRxN88gVqhYmZxEDQNjnQTYuZxBf+sfBiWss677dy6H4D3KCqJ7RCWsw5Hbetm0bli9fDgB46KGHcNJJJ+FXv/oVnn32WXziE5/AHXfcYeEjTuTKK6/ElVeWP3w8/fTTU147//zzcf7559v2PDOVaotrMpfH7U/vwM1Pls9gOu/IWRhNl9+4jKZ5jGV49Y/eDpQS7MDciRl+donhMqwHgdknIL3zMWT2PjMhoi6k+gFJAFgvPKGOKneRUezu87BoCEz9803m8rjjn0Xb72bke8YHt2NgPGuJ3fVEMJVSbn+FbEpAjnxHwg14NbcIR/u3IbP3X+AaFtR8jsnYqU8JFG1frkxEifIZGfN7+BYs54qZtXZRNstpxZcQWvH5usuGmEw5J3GwcHDO7H0G9z71Bm5eV9QS0jPfjLzxa+Q2rUZ46YWIHPYpDP71k/CE2jH3PzfXfC7F6aHXdpIk4jT2MUACdreeDztDZ5Ui75oOUVWc82bG/GiaRza6FIBc+m0kAl3LSSwJfMVMF/bIq3HVTzZi88FzDUW+q2aqLrscqV1PApKA4LxTDWd7hLI7CzdcNOVaMpfH/2fvvMOkqq///753et0+uywssFRBkSJiASuK3cQSS/zG2H8W4teo2BsqiUGJCpbEnmJijH6NRhMbaFRUQBRiRUD67rIFtky/M/f+/rhz78zsTrkzc+/c2Z3zep59Hpg75e7Zz3zKOe9zzjOrW1Oqx7rWd+Lo8bWarrF8JIjA1rew+x+nDlAQZVQZr30A7mlXwDX1yiTlnIQS9Ve+3zmJzH+7q+APBtHpC8NuMsBpMUKIhLB3lXiv9unX4zcftqYc8xPrHPjJRHPGMRkN9cBYoNJWiIYQav0EHW/+LG77qVei9pjfpW10YzKwqLabsMfPob0vjFpH8t/eXDMZVbPvwZ4PbsCeDxbANvJomCqaEdixAqyttqAmOkrJ9n2Ohnrw9Bde/OKVr1DrMKPBZUFbXwidvjCW/ni/pPmm0xdOSvctxr4yHR6XBZd3X4qjh0fhOfRGCODB+9vBWioR3vMtetaIdU+Hnf0fgGEHzFWufS8Aw7CZx7zZCcZgluf5aLALrNmNwM4PwCjQo2T6Tpkqx2cNDghRDpZp18MF0UEl3b9j6lUwTr0OvZwJ1dok2mVE3NP3FcVpw5oc4EN92P3PnyDq3YXGc1aCrRgYZOqP2mvs1Jijck1wHE7VeMxn+84GA3thNg8UKDAMg9q5j6LNvxuVMxckfSeUrpHpMyrEeb1i5nVZ38diYvHmhftj7sQGRPt9vi8cwf0J59gz7esAAOvD47Drv36csT+n6zk2HVoJbrJhG3UMetYuQWD7u1n3c9KYr7AaYe2XqSON+XneFsAI7Oar0B0uHf9BOmwmA1wWI/pCEbR7Q6iwFb8u71Alr52HIAjgedEb/u677+LEE08EADQ1NaGzs1O9uyNKlnT1JQBkVO49/OEPqLGbUZnmS1xpM6HCqu0XPN7cJblmYl1MdaBFjQlrYyz9u1+dSjnt29E4ICU8FbsTavWlWghE22+V/78jKjoqa4UWVNlNBds91wim7BROU59Sos5hxhq5TmV+DXW4WFMOkwYdv4EE26eoUVnImN/DeABo66hMq3L6+DaE93wP97Q0zStUakJQCOEIjz1+8R4S67KaqvYROwJGQ1i5+s2Ur1Uy3wjb/wlAbNBjqdkXfKATXNe3ipSkmetTXZXWdsEd76FWaEMvb8c31qOyfk6h5KucyxSQKnSer2rYD2BY8ME9sqo8F7Iq48zOlDWinAdci0MeXYMvW3vzjnxnr7f3BKzDDimoK3hVZAcAwFI1bsA1E8umVY/d9fYGTdfYTIrJvq+eydrYIxAMwjYjf5VvoTXh0v7tZt0C97Qr0bJyEQyIynsb3+Z/QIj4YXA0wj3lsrRj/qEPfoDBmnlMGgoM+si2798IaPWv0PPZAxnrc0r7m3QHWPe0X8AyfA6MzhGI9O2EEA2h6pA70XTRRrimXFrQfSsh2/fZYKnAQx+ItbY7fWF81dYnOyNLYV+ZjjqnGZ7a4Rj1k38hvHutWMcvNh/4N76CYT9ZDtf+l4K1VKStZ6d0zMvzvLUWLX87HO2v/gjejS9mvcdIJJy2/qd9zCnZ/zZmJ87883+xtuLnSWrXz9w/x3FPfwGnpfipmEDxO/GyFhdYswt8oBOBbe8qek1bn7geqHWWkhSVmzFJ8zGfbVxYbZXwhVKvsQa7B8NOG/idULpGqtHp/tEfTcABPc9h55NNAz6/v+2lMj0fBKeV9nwjn2OL66i0NM4GY7Ai6msFt+ebjM/NVDrOxLJ45KPNqDOIDRrbo9XytVK2O5Cc/k2oR16OypkzZ+Lee+/Fn/70J/znP//BSSeJhdW3bNkip2ETQ5tMUcDuYPpI0+YuP3xcBFfPSR1pvHpOMzi+8A596Yj6doPb+z0ABpaY81BCy02NpN4MtqxM6oQud/zOtZFOmohRf9tvj4jRzAa2He9u2F2Q3XNtHMKHvQi1fw4gfX1KCY/TgtVSnco8GuoIAh9XVGrlqOzVZszvM3oCAG1TvzNt6jrfvVyz7oRqIH0fDSyDalvcUckwjKyqnMp8nvK12Wx/6ywjuM7/AowB9rEnw+Coh9HdDEBI6qqbDtnpMeuWFE6P+QCTOvggpVf+w3842vylW8smU7H5Quf5CGuRu1lL391cEA/v2R3s/Z20JrMTO3sCAAqb69MWyjdXoGrOooK7gjcIYuqTq3bCgGuJtu/vtNF6jc14QPxiGQzWmoyHV7PVhRv+tSnvJgNqdFNN9bdzTDoPrS8dC+M3D+LPE/6Ov549EUI0DEvDQWi6aCMazngL3qgx7Zhfs7MHYS4M19TUDiXXVLF2aCEUcjiP729S3wPDGmLNgFYguH252Azo2YnY8VQzer9YptjBni9ChvRh97T5CHEhbO7yp7yu974yE1Kn+8C6B1M6mHvXPyYrVtN2ls5xzDMGE5wTzgIA9H7xSNKeMxW727aiYtpVA9exg26De/p8hLkwHGn+No6pVyHMhXHwqOqUgZPjJ3p0s32dDo4D28hYB+TtyjogSzUqU6XA5rrG1rF70WjsRFRgccSsYzS3e7byKu9tbENHmkZGPOdDz9olea+RhZTdkT4/vG5J2gZSfMQn297jMGDuCA6srRYfBqeV/HwDFN9Zxhqt8jkvW/p3pnNsd5ADG9oDEyP2jOiIVsrXStnuQPEDI+VCXo7Khx56CJ9//jnmz5+PW2+9FePGiYeNl156CYceeqiqN0iUHjyfuRBupTWzcs9hMuKmo8fhjmMnyM+rtJlwx7ETcNPR4zQthBtsEdV65tr9YLBWJV3Tsp6NxXMAGIMFvL895igVifTl1vE7U0MXYKDtd0erEBKMMDMRPLniE9xYgN1zPSSFWj8F+AgMrpEwuUdlfG+P04y1oX0ggEGkexMivtw6YEd6t0PgvIDBDFOlNvX+MjUxKmTMz5s+TfwdtFRUZtjUhXevgd/vhXPGtap3J1SDuJLVApZNdupJddMOt3+Z8rXZbP9zzxfi+zQdCYNVjNxKzSpCrZ8qu0Gek2tcivbbCcuwg9H64lHo/vTuAU+PBjrh2/wqAODvvrkl3SUwPua1medNsTIN+TTUYU0OuGf8b8rDdTaHlVqb+bT19qomFNQVPBzoRjXbDQCoG7bPgOt6rrGZ5pJIz2bwnC/t4dU1bT7e29iG9zfvyVvlC6jTTbX/55urxqNq9t1gq6fgzP95CON2P4XtT4zAzmcnYMdTzfBt+BuqLDxmjaxM+X6VNhMYox2VaWqHVs66oeBGOoUczpUcogzW6oId7PnCmhxi+vCsW1N/n42Okt1XZqLeacGR4+szNKt5VJH6K9cx75pyMRiDFeH2zxFq/STje/etuhOtf58LX93RKd+fMdphnnrdgL+Na9atME+9DozRLts+wgv4qq0PEV7Q3fZ6OA7so44FAAS3vweBz67Yb8/Q0CXXeX6aWTxb9FjH4vpj9tfc7pm7T1+Xsft0oYpIJbWGM5GtgZTFZMG8CbV488L9sePWozHuR39G00Ub8avzfoJZIytLdr4pVl3WVNhGxepUbs/sqMzUxKjSasJYu9hlvTNagUhChcJSnucBfW0/lMnrL7r//vvjyy8HHgzvv/9+GDQu3kvoz55AGFFejNCmigJyPI+r5zTLXbgSkSIeDrMRC44ai1vmjkdPkENFrEtv/3oVahNPRR6o8NMyEsUYLbA0HITgrg8QalkJc/VEAEAkpqIzuFJ0dk2B5DhIpXACBtqehwG7Ih6MMbXg5KYABEHI2+65drqVlJG2EZnVlIBYV6VPcKDLMgG1oQ0I7foIxglnZn2dBNcVa6RTNRGMQRvpf6Y02ELGvHFPL7ohdnHVimx13cxWF1p9PEZWmVXvTlgo8TSRgfdjaxIdlZONm1HB9KFHcCVdz2b7nv8THYaOsT+WX2MddjB8G15AqHWVovsL7voQ7f88E+b6AzH83JUARKUBt/c79KzdCMeEs5K6Wnq//QsQDaPPsS++4cagqYQ3Ne0ZUnTUmOfNNfvBv/H/8mqoI/BR7H71NFQc8EuMvHQ7+HCf4vqbHqcFP3T5NdtQFtoVvKP1OwBAV9SNkVUDn6fnGpu1RmSGLriumQvwywc/RVcalU1O96FBN1XH2FPRZZ2MnlTdp2O1z/501hWY+MDAIIZkd7PZCtcB16LqoJsQDfXAYKlAJBJWJehTSH3OOgWHqGzOg2wd1QvlxGfX4dYjf45jL70ZQji5ni4XjpTsvjITlTYT+nrbCpoPJHIZ8wZbLRz7nAvv18+i94tlsDamFpGEO7+EteWf4AAseGMrXrrm8AHvz/E8/vhFB8ZX/RxHXXxjckf1LzrkjuqlZnvJcVDMNFizZwZYSxX40F6E2j6DtfHgjM/PlJ2W6zzf/uH74NYDjWNmF83u6Wpgn/2Xb2Ldp9MoKgtcIzPWGlZQ6zgSzFITN9iDV38+Fd6196Pl6Xjd1RlTr8L7ly1AhOdhL8Ux79JHUQnEG1wGd/4HQiQExpj6nJrpHMvxPC6eYgJ2ALsT0r6B0p7ngcT6oKUrPhiMqFod22q1wmSiAqJDnbZYCmy13QSTYeAQcpiVRTwcZiPMRhZ1TgvMRrYokZDgztT1KQHAE3OE7A1w4KLqy8etwwfWqYz25Zb6nclpA6S2fYswDABw1tgQ7GZj3nbPNYKZySncH2lD+YNxatJrlRKW6lNq1EhHEOIq4lS2L2TMG2Ld3qO+FkUR+LzuP0OarDOWoiOlY5QamdJEjM5GmKongYGAJbP25GR7S6gNobbVABjYx50qv6dlmHiwCLWugiBknweCOz8AAJjrpsiPOcaeAvv40wEhis7lV0DgxTQWQRDQ97WY9u0d+VMApR19zeScV2Oelxpfhbtyd1QGW1Yi1LISnW9fAjBsTso8rSPfhao9uttFR+UuYTgM7MDSAHqusUrq5aVTf+0Nsfiu3YtOfzzYWWo0eprQl0ENO66uAvedOCmj3U0WsbmJUSo3UKCSUqKQ+pxKArGFplMWQpQX8PHWPTjh2S/REeAHfJ9LeV+ZCZZlYLNXFTQf5EvFdHGs+Db9A5G+HSmfszfWjOQN/yHg3APV24Bo00sOGolPdoaw75KVmPPUd9h3yUp8uiuESw4aWbK21yMNlmENckkaJenfco1KFdZYQ9daAIBzeGbnqNqkUsf7Oan7dOo1ttA1Mm3ZHYWd7g1ZPp8xWeFfu3hAanjf6kUIfPEArIz4e5XqmO/whbKWfFAbc+0UGOz1ECJ+BDNkJEnlDlI55x1mI84YJ+559qIGwOCY5wFK/daKvP6y0WgUDz74IF588UVs374d4XDyIrBnzx5Vbo4oTRJTMdNhNRlKLuIRDe5FuFNUAltSOCqrbWawDMALYt2vYW51016lz0x0wkmKSqNLmaOyPUuNSmCg7YVV78D35VrAuzXPOxcReA7uqVeiO1W31qlXIti6Cpa6/cFaKsBHguA5L1hbbdZGOkB8gv+an4JZeFH+OylFcnRoVZ+yO8AhHHNep1IRA/mPeYO9HmCNAB9B1NcKo0J1bS6wJgcqZvwSEAT0rn8sSeVkmnodfvnIatx/8mTVP1cNMhXeBgDbyLng9nyLEyu/wf/ceaVi2/s3vwYAsDQeAqNjmPy4uXYKGKMNfLgH3N4NMFdPynh/gZ3/Ee9jxOFJj9cc+SCC25YjvHstetc9gooZ/4tw55eI+tvBGG2wjDsLeOe/gyL1O53tC53nJUcl1/UtBD6qqKGYhH/j/wEA7GNPzVlNVydv5rWxfaFqD3/XJtgBdLIj0j5HrzU2k2JSTNG1ys8DktVfVaw4hwoCsMcf1qU7ZzZCgb1ZnXXzDxuNaw4fU/S9jVLbp0Jatzp96Q9RhXZUL4QuXxi8ADAMUGNP/d0oxX2lEj7b3okJU69C3+pFA64pmQ/yxVw7BdYRRyK48330rn8c1XOS927hzi/h3/h/EMDgkd6zcNj4wbWnz4bstCl6B+R58G18GYFt76Lq4NszPjeT0wZQbneBj4Ln/GBttbA0zFLnFymAuJo19Rpb6BopwsDcMBNNB26R1ZzBnR+AYbO7Njr7fHBOvQq9Kb6TFTMXgGXNGcu3aK0uzxepmQ4XFdATjKQtHaAFDMPCOvJo+L77KwLb34Wt6YiUz8t2jmUCYumvedP2x+5D5w2KuQbIPuaJ/MhLUblw4UL89re/xdlnn42enh5ce+21OP3008GyLO666y6Vb5EoNbI5DiRKLeIRavkYgABT1XgYHQ0DrrMskyDd1qChzrCDAcaASO9WuR6hlO5rcCpzTmXqtp5Iou0tVWPEz+pJ3T1QKcFdH8OdsuD6rXBPn4+u5Veh462LEA3uAQMGnpNeQNNFG2FQoBaVNpQttpnwnPIy6n/8GqL+dgjRsKK6WHIjHY0UlZLdK6zGjItlPmOeYVgYHaKNpJqlWtD1wQKxluKl25NUTtf/a1MsRac0o4BymkiaTY2kXuB2vZ+T7X2bXgEAOMadlvQ4YzDBUj8TALKmf/OhHoTbY3UuRyRvyoyOYag67D6YqvaBsXIchGgIBmu12JjjzHcxrFbs9t7uDRc98q0EQRCyNu8CCpvnjRVjwBjtEKJBcN2bcrg3Pv73G3+G4tdJaN2dsdCGL5Ee0RZ9lpEZn6fXGptvjUijgUWNXTw4lWp6lNmWXf1mN+m3t8nX9krGfKEd1QtBmmtq7GYYU2TqSJTavlIJT6/thGnqdajQoWGdO6aq7PvyafBccjOivZ+KTqIN9mPwfWRkxnkeGHy216sLry2WAhtqW41osDvt88R6/9n39NnsznM+QIjAc6JYR1FsCKgv2c5SadfIWbco/k6EWlai/bXT0fLCYTBYa7HrLwdh96s/QmDb21lf+8F2PxzTrk7TQOpq8OEe3dTlhWA1GeC2iuNDlzqVI7PXqczU6R4Aot4WAIDZ1Tho5hqAFJVakZej8vnnn8eTTz6J6667DkajEeeeey6eeuop3HHHHfj0U4UNCIhBS6YGC6WMVDPRkiEVWcuNDWt2wRyrVRfc9REEPipPyEoVlfnY3lghOiq5nh9yuNtk+HAfOt+9DK1/nwv7+DP6HZKuR9TfDtZWi9pjfofez5di+5PxjqE9ny3J2jHU4zRjH48TD555EMK712LHU83Y/sQIbH9iRNbXC1EO3J4NAACTVh2/MxR/VgMp/TviTZ2eVSgCH4Hv+7+j/Z9nItK9OSlFJxSRUnRK03EgF5t3p1EcjDhCDAB0bwLXs1XRe0b9HfJ8YB/7owHXpYY6mdJXgJg6WuBhrByXsnyDa78LMezs9xFuW4PtTzRhx9NjsOOpZgS2/AsNDgb7eJwIR3n0BrVJ+S+E3mBEHhvpSk0UCsOwMNVMBmurRaRvu+LXhVo+RtTXCtZSKW+Mc6EYSptEh9KIizai6ZItcO1/maJahUavGFQK2/U/cKYj32Y4pb6Z/3Rre9oOx1o765SSj+2V2F2Njur5Mlj3lUoIRXgc8uga9Iy9sugN6+zNJ8HobgYf2gvvd3+VHw93/Bf+Ta8AYPCG8XwAQ8/20pj3c1H4QsVbY43ukTBVTQSEKII730v7vL0BDhG53n9+aywfCaLnswew/YmEPffa7HturVGyxiaukU2X7kDTJVtgbpilqLkUANkhaamfAcZohn308QCAvq+fy/razW27sePFY2Gun4GRl+7o9520FJyarid6rrHSfiy8ey2iwdTZtdnm+ohPPBcbnI0a3KF2lPreZrCSl6Oyra0NU6aI9bicTid6esTIwsknn4w33nhDvbsjShIpVSFdQ5dSRa6Z2Dg77XO0nmisjfH076i/DRCiAGOAwT5Q4ZkKpWrWREwV4mG3EEXl3k/vRdS7CwIfhqlq3IBDkrlmMupP+XveHUM9TgseOmkMwuuW5Px6rvt7gOfAmF0wujIrkPIlWwpsoUhOrqhGDXXCHeshcF6wlkqYapJTmWsdpb24ZqqTCIgBANmxuCP9oSAR/w+vAwIPs2c6TBWjB1yP16nM7KgMxOpT2kakTnERIgH0frEsxZhehJ41i7H0lLEAStP20ph3WYywaxjNrjlqKZou2ghzzWTFCmrfxpcBAPYxp+SVMinP8xrXZZUcSt2r78OOZ8YjsOVfil5nD20DAAiusVreni4oaeqiJ798/Qfd1G9aItXgzhaQUqOjej7ks7cZLNQ5zfiu3Yvfr+nIu9N9vjCsAe5pVwAAetctk9X7Um1Kx/gz8N+AGCgdarZ3mA2wGsVjbtFVlbHu34Ft76Z9jjTmq2wmWIy5p7XynA89a36D7lWLct5za41S0YccdLFWY9efDkD7az9GaPdnij5Dsq1t5DEAANdk0eHu/+ENRP0dGV9bu/sVGLv/iw1v3QCk+E7qqS4vFL2UxIB4ljHVTAYgILh9xYDrgiDEyx2k2dNHfa3ieyWUZBoM1Olo96FMXo7KESNGoLVVHEhjx47F22+LUY01a9bAYhlaCx0xkN0Zij+XKjznQ6j9cwCZm7to7qiU6lS2fCSn+RqcjYpqswW5KHpiyqtcbC+lgfDBPRnTUNIR7vgver9YCgCoOfIhsEZbyuexZlfGmi6ZoqT1TguOHF8P7/r0HUfTvV7qGGyu2RcMM7DxhBpkc5YVilFSVMZKAqiNrCZuPBQMkzzt69EZMxeUlDuQun8H21Yres90ad8S1gbR8cl1fZsxxScYq09p7VefUiJbF93Dx9aj1mEuyY1NMRwHfCSIwA9vYMdTzbKKOquCWuDh2yilfZ+e1+cWeyNvsFSCD3QiFCsTkAk+1At7VFQiWKrGaX1rRUePBhdK8YYi+HxXLw55dA1cM64turNOSyS7dwc4hCOZm4Tlq5YtBK3XWD3RW2njnHwBGJMDXNc3CO76COE9G2J7AgaVB986ZG3PMIxutreNEp1ngW3vpC3torSUUzqy7S+UKhO1IFe7iyV3pgMAAlv/nfX5EV8bwp3/BcDItjbX7Q+zZwbAc0nq4f4IgoB9e14EAOysPzfluUFPdXmhFCsQmw7JcZwq/bsnGJHr/adP/Rb9S4NVUdnlDyOiQUPeciUvR+Vpp52G5cvFAfiLX/wCt99+O8aPH4/zzz8fF110kao3SJQegzHyHWpdBfARGFxNMLpHpX2e1hERqfM31/VNbJFV3vFbWvDNBjanAsms2Sk2bAEQ6c1NVSkIPDpX/AIQorCPOw325uPTPreQjqEOiwFBf/YmBqmQ61NqlPYNJNRJ1Cz1W6xRmq4zZ6HEO7Cn6HZfwo4DQFnzLvuYk+E55WXUHPFA1tqmfKhH3kDZx/045XMMjvqYg19I6/yMBrsR7lgHIL2jMtt3IhzsRoPLUpLqsvjhVZu0b1kNsjo3NUio5RNEfS1gzG55Q5wrxT68SiU/wgoclVKtzs5oBWoqq7W8LV3Qsg50oUhjftteP4zm4jvrtKTSaoIx1kG+I0NDHb3Qeo3VE72aukgYrJWomLkAnlNehqX+ALAmO5ou2ohhZ38Ac82+mmeM6IlHJwW3dcQRAGsSa9L3bE75HCV7m0wUsufWmnyCgbbRJwAA/FvezPrcwDaxo7q5fgYMtlr5cde+FwAA+r55Lq2DOLjrQ9RHt8DPWxAaeVbaz9BLXV4oeiv7EutU9v8bSGusO029f4GPIOrfDQAwDDJFZY3dBIYRmwV2+UtXcTvYyMtRed999+GWW24BAJx99tn44IMPcMUVV+Cll17Cfffdp+oNEqXHYKwlJCnKrMPnZFTdaX2ANdhqYareBwDg2yBG9IzO9J1dE4lHX805KweNcvp3bnUqfRv+hlDrJ2BMDtQc8UDG5xZS04VhGFjt1Xm9nusUHZUmjRrpAHHbazXmtUz9FgQBwZaPAcRLDySi10ZeCVyUx57Ygp+uRiUAmGomK65t6t/yBsBzMFXvA3Psu5gKKZ08Xfp3qGVlxvqUQPbvhMVWiba+UEnaPj7mtdmU56sGkdK+HWNPAWPM7/sod0D2hxHltW9kZPGISpFw55cQ+My10iRH5dbIMM1sryelPN/E9zZWzdT5eiE2Cyzd1DSt60DriZ6pmBIVM66JrZGjsePpsWKt5K1vgY8E0egW55mhaXt9ArGsySGXmUqX/r07S/3trJ9RwnUUE89SSpsF2kbPAwCE2z9HxNeW8bmB7clp3xKOiWeDMVjAdX6VNjDY99/fAwBe9R+Ouqq6jJ+jh7q8UPRWcFtHHJ7WSS810kk314hOSkEsiWbL/LcpNcRmgaW7vxms5OWo7M8hhxyCa6+9Fqeccooab0eUOHJ9iUG0qYkrytKnfQMJabB92m1qpHuQ0kalRirZKCQ9R26o053dUclzPgjRMKL+dtjHngrPKS+h5siHYHRl7kxeaE2Xz3d05tXEoBiKynaNVcRGWVGpfuo3t/c78IFOMEYbLPUzBlyXFCyleHiVFnsDy6DallrZF1fmKatt6tv0DwDp074lrHKdytSdvwOx769txJFp3yPbd2LD7r3o9IVL0vZaK5zyUYOIad//ByC/bt8SSZFvn/a2N1aOA2NyQogEwO3dkPG5iY7KoZaKCSSqy0pwzA/CbJFc0PsAm4mhmn4M6G93nvOh57P7066RD57UDIuRlbsFDyV0bSwip3+n7kItr7F5jvlSrqMoBUUivIDugLL7MDoaxNRtAIGt6Tt3CwIfr085al7SNYO1Sm6QmKqpTsTXJpf+ed533JCc6/VWcItO+kMBAIFtyenfWetTSmnfjmGKSqKVGqUciB2s5O2o/NOf/oTZs2ejsbER27aJhd8feughvPrqq6rdHFF68LwgfwEHy4ZSiIRkZ0Oq1NdEirGpkdK/WVsNTDX7wVSprA5ZIek5ShvqxDsIiqq0HU81I7z7czgmnp31Mwqt6fLcF505NzHgOZ+sEtU09VvjQ5RBUlT6WiFE1d1cBnetBABYGmalbDyST+S7WEh29zjNYNnUCqdclHk850ekbwdYW23atG8JS4KjUhAG1pvJVp8SyP6dWP6DH0Bpbmq0HvP5qEFCrasS0r6Pzfuzix35ZhhWTv/OVqcyvHcjAGBbpGGIHqJKdyOvdbkDvSlp2w/p9GN919hsa+RR4xswyeMccipiQN/mXXJDnR3vp9zXFRoYYU0OVMy8HpWzbim5OooWowEVMcd3LoFYqbxUpjqV4Y714AMdYExOWGOZL4lI6d++DS8MyKrxfv0swEfweWgivuWah+h8o7+CW0r/DrYlB/qzzfORWFbZYGukI1HqpbQGI3mFzx5//HHccccduOaaa7Bo0SJEo1EAQGVlJR566CH86Ec/UvUmidJhTyCMSCxVLt8oYLEJta+FEA2CtdXBVDUx43OL4qgccSQ8p7wM28ijEfW3w+gYBp7zZd1UyE6bPBZWKfWby1CjUoy6P4DuWDdIIBZ1X/0rgGHF2ixZ7lGq6VI56ybwoR6wlgoIPKeopks4IuCQR9fg9Z9fgeZZNyHq3w3WWg2ue3Pa13Nd3wAADPZ6GOzapQlofYgy2D0AawJ4DlFfK4xu9bqXx9XEqbvd1zmSI99V9tI5pLcpcJYpUeYZ7HXgOR8YxgDPiX8R7Z0Fc+0UMEYb+HAPuD3fwVwzWb4WDXYj3L4OQGZHJZD5OyGdC0tRXSYHpFzajAdJDdK96t4B19zTroLAcwMc6/Fu3yfnnfYt4XGai6pmtdRNQ2jXR+K4mfQ/aZ8X3CMqKrdFh6HWUTrfRbUo5Y18WwFr7GAg3mSh9Gw/lNWskuMgwPHwhaNwWoqrXMy2Rgb93djH4yzqPRULPRXc5rqpYG21YiO1tlUDhBJqBAO93z4Pc/0MNF2yFQLny2nPrTUepwU9wQjavSFMVDi+bKNPRPeqXyGw7V0IUQ6MYWAJGKk+pa3pyJTBd2vTUTC4mhDt2wH/5lfhjAktBD6C3i+fAgD82XccTAYGVTnU+x8s6K3gBgB788kw1ewrn3NZSyUEngOfxX8gdfw2OAe7o7L0goGDlbwUlcuWLcOTTz6JW2+9FQZDXJo7c+ZMfPnll6rdHFF6SLLtKpsJZqMqlQM0J7hTWX1KoDiRKNZaLdfT2/nsRGx/cmTWTrcAsNubWTKfCVPFWABAJEPqt1odBPOt6VLnNOO7di8eWdUOxmBGuOO/2PHMeHT869y0KgQp7dukoZpSEISs6QqFwjCsXKs04lU3/TsUU1Smqk8JAFaTQU75KjXngZLDazZlHmNyguf8olL4yZHY+exE7HiqOet3TuxCORPAwPTvUMtHAASYqsbDqKAzYbrvRClvarR2HKRVm866Be5pVyHa72CtVtq3RKk21JFqOvWYRsKQRkU8mCnpMT/IskVypVQbGUV5AR0+bddYPXFYjLDHGkfoYftsa6TVXgl2CKopAcDj0k9RyTBsvLFIzLmWiBprbN+XT6D9n2fCv+kfJVdHMR8Ft6X+ANG5G+5BME19cNlROSp1VgXDGuCa/DMAgPfrP8qP+7f8C9G+HeDNNfi3/xB4nJYhqSIuhTXWWDUuZd34y2bVYx+PM72i0tcCADA4BlfHbwk9FdxDlbw8TVu2bMH06dMHPG6xWODzpe7USQwNBmPBc6X1KYH4BO/novCFMjc9yIdc6+klsrs3cxHiTMjNdPq2p00t1ruDYP+6KtYRh0MIe8Ht3Sh3V+5PuPMrAIBZw0Y63QEO4aiY+qul2sPgEtO/1axTGenbgUjfNoAxyKnMqSiFjU0qlDTuylinaeqViAZ2o2fNYnSvyq27NBBvqNN/wxzY+QEAwDr8CMW/SypK1e6A9s10gNRdNa0jDkfri0ej459nQYjE7RJqXY2odycYsyvtASUXim17qaFOqGN9ylICgNiRng11AgDC9uai3FexkQ6vvcEIglxU57tJpr0IY15P5BrcJRaQ6vKJTa0YJn7QG2romY6ZaY10TZ2P9za2wWYefPXglKC3gltO/07RUKfQxqTi3ng9wBhgG31c/jepEfnYnmENct3JwJZ/DbjOh71yc8hM+wDn5PPF99j+LiK92wEAff99AgCwp/EshGEeVOfYXJDmmi4/h0g09V5DSzKdc/2fP4AHT2pO30wnVqPSOMgVlaW2xg5m8nJUNjc3Y926dQMef/PNNzFp0qRC74koYQZbHSGBjyDYGut4nKU+JQA4zAbYTOLXQouNTSGqxUJsb3A0gDFYASGKiHdHyufo3UGwf+SbNbtgG3MiALH7eCqK0UhHsnuF1QirSbvNvKSojKqoqJSc9GbPNLDm9Kk3pVq7LF6jMoOiMksdSIO9Hr3rH0v52mzfOblOZb86O3J9yqZCHZX61xJKhagiLo66rL/a1FgxBlH/boR2r8GeD2+UnxdsWSnWFh1zsippbcXugGyqngTGYIUQ7pXr6vZHaqTTEa1EhauyKPdVbCptJhhjStGOIjQyyoXdGpc70JtSDYxIdq+xm2EyDI5MnVzR0/aZ1kjbjOvxyze2DEklK6D/mJe6Uod2f4ZooEt+XBAS6v3neZ6SSqHYRs6FwVZT4J2qT74KbvtosU6lf+ubA64Fd/4H4DkYK8ZkrO9vqhgD64gjAAjwbnwZXO92hNo/B8Dg+6qfABia6m0AqLabISVjdOqwxmY653rXP4qjxjegqTL1Hk5O/R6kisp4MLC01tjBTF47gmuvvRZXXXUV/va3v0EQBKxevRqLFi3CzTffjBtuuEHteyRKiMFWRyjc8V8I4T6w5gqYa6dkfT7DMJpubApRLRbiOGAYNq6qTJP+rXcHwVTRV+eEswAA3u//nlKFxBVBUVmsMW+MdX+P9O1S7T2Vd7svzbpx0v1ks30qZZ5YU9UOIdyX93dO6vzNdX0rPy8a3Ku4PmU2JLt3+cO6RL7T0ReKIBiRVMTFddqYKkaj7vhnAQCB7SvAdf8AIRqGY8KZaLpoI6oOWajK5xT7AMuwRnkNStdQZ6h3/Aa0X2MLYbDtb3Kl5O0+RMc8oL/t062Rt7z1A75r9w7hMR9zHPjCcn28YmJ0NsZKEwkItHwkP743wIGLSvX68ltjJUelGqVQtCDfQKxt1LEAw4Lr+lpWQ0rIad8xB3Am3NN/Ac8pL8O9/2VgGBZNF21Ew09WwGsW99pDtRaxgWXk+tZ67OmV1MRtdKd2VMZTvwe3orLUzlKDmbwclZdccgl+85vf4LbbboPf78dPf/pT/O53v8PDDz+Mc845R+17JEqINgUKp1Ii3PUNWFstLMNng2GVqeHqHNqpywpRLe5W6LRJh9xQJ03nbznqrlMHwVR2tzWfAMbsQrRvB0L90m+j/g5E/bsBiGolrdC6PqVEvEZlasVrPsiOysbUjXQkSrWuSlus3IGSMZ+uDmQh3zmD3QNjxRgAAoJtqwFINhVgqppQcGfCGocZDAMIgpimUypIjgOnxQC7ubiNHwDA3nwiqg5/AMN+shzeb/6I7U+MwM5nxmPHU83wfvPHrPV8lSDNN8WMfGerUyk5KrdFGobsIQpIOMD2ldh8M8QdZnU6Hl4zMdgydfKhtgTU86nWyM93igG4oTvmxd8rygvYG9BnjXXucx48p7wM+6h5iPrbIUTDMPFB7ONxotJmgsWYe6ZOYtq3feypGtx14dT1K+ekFIOtBpYGseyOf+tbSdcC2zPXp0zENupYuU7ijqfHYMdTzQhufxfnT/dkrJM4FJDGfanWxK1M08Qonvo9WBWVpRkMHMzk5agMBAI47bTTsHHjRni9Xnz66ae49tprMWLECLXvjygx2geJ4oDnfBCiYdiajkDTRRtRPedXil+rZUQkX9ViKBJFd2yTla/tTRVjACBt2iEg1nwUOwhuSVamFaGDYGJtDynyzRptcIz9EQDA2y/9W0r7NlaMyZjWXCjFOkQZZEWlOqnf0UAXuD3fAkjf8VuiVKOAajS3KFQpLNWplBzlQak+5YjC0r4BMfJdYy89J3EpNBVx73cRetc9mlc9XyVIjsBijnlzrE5luH19yuuRMlBUAqU53/hCEfjCYs3MUt/f5EviISpdgzo9GOpKVqB0D7BDvdyB2cjKThG9bO+eernoMHtylNxYhPvyIXxy1YE4ZFRlXu9Z6mnfQGElhezNYvp3YOu/5ce4nq3g9m4Ua3I2HZnx9WKdxMUp9g+L4P/8fjx4UvMQX2P121dm2nM7pl6F9za2yU7sRPhIEHxQLI9gGLSOSv0DUkONvByVP/rRj/DHP4qdtMLhME499VT89re/xY9//GM8/vjjqt4gUVoUWvy5GPCRoNjh94kR2PH0WOx4qhm+7/+uWIWj5YYyWz29dKpFaSNvMjCoShOJyoYx5qjkMjgq/RtfQvs/z8SeD28uegdBSdUX4QV0B+POI0cs/du38WUIfLzBUbw+5WRN76utT7mqrxDiNSrVSf0Otojdvk3V+8Bgq8343P6NjEoFNZp35fudk5DSv6XO33J9ygLTviVKsT5oW6/+8zxjMOddW1QJetg93lDni5SOIq5b7Pi9NTIMDe7SXWMLpRTHvLS3sZlYuCzFVxEXA8nuwQgPb6h0Ghm1lYWjsjQbGRWjaZre6Dnf8JwPPWuXDHCY9ay6F9z6Jbjz6JF5vW+pp30DhQWkbKNPACCWf5Ga6klp35ZhB2etma+kTuKoKlvO9zVY0CMQK5Fuz11x0G0wTb0Ot7+7HbYU9f6l+pSMwQLWUlXMW1YNacz3hSIIlFizwMFKXo7Kzz//HIcdJtY8e+mll1BfX49t27bhj3/8I5YuXarqDRKlRSnVEpJUk1IqBc/54t3GBnT4XaRYhaN1GmzaenoZVIuJ6ccMw+T1uSapRmWa1G8A8P/wBgDAOiJ7h3S1sRgNqLCKB8Sk9O+Rc8Faa8D72xHc8b78ONcpOipNGjbSARJsX6QalVFfG4Ro4ZuL4C7RUWltzN5EqhQdB1yUl9OhC7V9Pt85CUuCozIa6BLTraCmo7L01GWlkIpZSD1fJehhd1PNvgBrBB/oTNk0KzH1uxTWWK2oK8Uxn7C3yXeNLXUcFiPssQNiKc317SW0r9SKUlRUBrkoeoNi8HdoO4n1m28yOcx86x/F9Kbc1ZCDIe0bKGzMm+umwuAYBiHiR3DXhwByS/tWUidxeMXQdc7n28hILaQ9d9Ol2zDiou/RdOk2CJOvxiGPrkFPIJLyNYmNdAbrGuy2GmGONYQrNeHHYCUvR6Xf74fL5QIAvP322zj99NPBsiwOPvhgbNu2TdUbJEqLUol8J6ompZ/edY8X1FVbQlpcteyWlq6eXjrUcBzEa1T+kEbNswnc3g0Aa4RtZPaNgBYkpn9LMAYTHONPBwB4v39RfjzcpX0jHaB4hyjWVgcYzAAERLwtBb9fSG6kkzntG0htd72RNliJ6dGFkOt3TsJcOwWM0QY+3IO+r56GWJ9yYsH1KSVKUc2qpNu61hRSW1QJiZHvYJEi36zRCnOsnm7/hjrRYDf4QCcAYFtkmO5rrJbE19jSGfOlsrfRmsTmIqVCKQRGtKYUFZXSPG82sHKQeCiip+2zOcwi4b6UootMDIa0byBu9z1+LudmgQzDwJbQ/VvgIwhufw8AYFfgqFRSJ7HCUlhWRilTCvM8a3LAv/Ut7H71dLS/fjZWbPVnbNwVjZ17DM7B2UgHkJoFUvq3muTlqBw3bhz+8Y9/YMeOHXjrrbcwb948AEB7ezvcbreqN0iUDjwvyM4DXZU2aVST3u/+gqi/o2AVjsdVeuoyNZSskqNSCPeCD+4ZcN2/RawFY22cA4O1Mu/PKYR0yj7HxLMBAP6Nr0CIiPW14qnfGisqizTmGYZRLf2b53wIdYiOEMtwJYrK0lN7SGO+zmEGy+oXXWVYIyz1M8HaauHf+jZYW60q9Skl6kpwU1MKjoNCa4tmo8JqhMkgjqtibubjdSqTHZWRHjHtuz1aCZ9gG+LqshIc82XjqCzdub4+z+7Hg4GStHtCfcrBqmBSgp7qskwOM3P9gbBYHANEFz2fLclYqmowpH0DQLXdDGnrlo/wQ6pT6d/yL4Ta1oAP94C1VsPsmZH1tUrqJNaWwXyjdwDcXDkOXNdXCLZ8jN1Sc8w0e5tITFGplghAL0pxrh/M5OWovOOOO3D99ddj9OjROOigg3DIIYcAENWV06dPV/UGidJhb4BDJNbkxKPjBJ9ONRn1t4G1Fq7CKeVUzEI6wbJGGwwOsUBxqvRv/5Z/AQDsY07K+zMKJZ3trY2zYXA0gg/3wL/tbUT6tkMI9wGsCaaqCZreUzHLHcidv/tSd/5WGnkPta4C+AgMriaY3KOyfq70fe7KI/KtFaXkOKg+8rdoumgj6uY9iaaLNqLigGtVe+9S3NSUQipmobVFsyFGvotve9lR2bEu6XFu70YAopqSYYBax9A/RJXSmJfX2CHsIAZK2/alMNdrhew48MWbBepNKZVy0hI9x3wmh1ntMb8TG74MKFWVvmHcYEn7BsRsGGkdy6tOZdNcsVRKqBu+zf8Ea6uFreloMGz2LumZ9g+mqddhwb+35l3vfzBQKsFAU9UEgDVCCPeir3s7gPTnWFlR6RicjXQkSsX2Q4W8tP5nnnkm5syZg9bWVkydOlV+fO7cuTjttNNUuzmitJA2NVU2EyzG7AuFVqRLpeADnQhsXwH3tPnoXnXvgOuSCocxZD4AluJGvk2FpiIAYKwcg6ivBVzPZlgaZsqP86FeuZuxvfnEgj6jENJFvhnWAMeEn6D3i4fh2/A3eaNiqpqQ9e9ZCIIgqGZ7JRhcw8HaasFHB449qdxB77pHwYe6wVoq4Z42X3Ta9Ku1GJTSvhuzp30DYuSbYQBBECPfDW79a/fsji3yejfu4iNB+Df9Q5Hd8yGellaC843ODV2kOkeVs24CH+oBa6mAwHOq2B0Qbb+rJyg7ZouB3FCnfV3S41xCx+86hxlGQ15x5EGB1nWg80GNxl2DgTpXaR2ixEwdaa7Xf93RCslhE+UF7A1wqCmBQEQ5NNIBxKwMQJ/Ub8lhBojlp6Q9hOuABTBXT0RbhlJVlbNuGvD4YEn7lvA4LWj3hvOa61mLGw2nvwVL/QxE/e2oOvhW2Zml6PUp9g89/gAOeXQN+kIRXTN1tKZUzrGMwQxT5Xhwe74Fur8FMCLtGispKgdrx28JveuDDjXyLkrS0NCAhoaGpMdmzZpV8A0RpUupRL2lVIpUzsru1fdh2E+WA0jeFOTiWEisZ8PzQkksZu0qqctM7maEdn00QFEZ2P4OwHMwVY2HqWp8QZ9RCJkiUc6JZ6H3i4fh/+F1mCrHAtC+PmVPMIJwTGFYjHFfccB1qJ37GPjgXgjRsOiUMTnEcgefPYDuVYvk50qRdwBiY5gEhZnsqFSQ9g3EIt92Mzp8YbR7S8RRWQJqj1ztng8lqeAuAdtLSPY12OsAQNXAhB62N9fuD4BB1LsLUd9uGBz1AOKp31uHeH1KINnugiCURNppKZS1KQalcoCV6PKHEY0pDOuGcCqm2ciiymbC3gCHdm+oJByVamTqDAb0HvOpHGafbe/CPt49WUtVSeuexGBJ+5YQbd+Xl+35SBDB7SvQ/s8z8g4S998/rNyxF9+1ezFjeGE1rksdvcd8IuaayeD2fAur93sAI7LWqDQO4hqVQGkGYgczQzdkT6hOW4kcXjOlUthHHwcIfN4dfgGgziH+fhFeQHewsDpoaqGW4yDeUCfZUen/QUz7tjXrl/YNZK6rYq6fCWPFWAgRP7wb/g5TzX6w1M8c8Dw1kezuthphNWmrIhaVe69gx1PN2PH0mIRaRQEwrFFxkyghGkaobRUAZY10JEppYwMAbX1iPRs9D1FqNOfKRqnZXRCEkglKaY0etmfNTjkYFEpI/+b2xhWVeq+xWiMpnEIRHn2h1B1Ai02p7G+0ptSaukhrbI3dBNMQVhED6Wtw60W8vIr+TlMtKQW7JzbzA2vCj//0JSy23EpVDaa0b4l802DlXgSrlafFK6F89jai3b2hKPxhfddYU6yPQGVI3OOkW2OjvqGS+l16zUkHM0N7V0CoSqnUjMteu8yed4dfQIx8V8Zql5TMhlKlxdVUOQYAEOn5QX5M4KPwbxUb6dhLxFGZyu4Mw8A17Qp4TnkZw/9nDepPfRnu/f9f3hsWJRRLWZauQVT3qnvR99WziAayR94lQu1fQIgEwFprYIp1GFZCKWzmE5E2troqKrN07FTSnCsbpVbPxhuKIsDFVMRD3GmjVyOjVA11pNTvbZEG3ddYrXFYjHCYxcBPqYz7UtnfaE2pBUbKxXEAlJ56vhRqEReDUrP73gCH1t4Q3t+4O33DuKlXIrDrQ3Ddm+XHAjs/EOs0DpK0byD/NFitgsSllC2iJS6LERaj6OLR22FmrhUdlQ1RUSCTbq6XU7+pmQ6RADkqCcWUUpqIlEqRr2oyG7LzoK80NjaqKSrdoqMyUVEZalsDPtAJ1lwBa+OhBb1/oWRz2rj3uwTh3Wux46lm7Hx2IrY/OTJrh8RCKFrH70ybsi+WwWCrURx5D+1eC1PNfrCNPh4Mo3yKl77XpbKZLwXHQaaOnUqbc2VD2tT0hSIIcNGC369QpDHvMBvgsORdHWZQoFdnTLlOZUxRGQ3uBR/sAiA6Kod6Qxeg9Dbz5eIwKzm7l4njAChB25fLmI8pRvcGOIQj+jcLlMb87e9uSyO6uBXu6fOx571fov3fPwfXtwNCNAz7qGPQdNFG1Bz5oI53nxt5Kyo1ChKX0jlWS8RmgaURBDdVTwYAjGS2gQGfcq7nw31ig1QAxkFeo7LURB+DHXJUEooptWLzhagms1FKG8pQJIq9ATEFvdDmFqZY6nc0tvEBAP+WNwAAttHzwBj07YKXye5ivcD70b36V6qmgmSiWI10Mm3KIj2bwXP+jJH3YMvHCHdvAc/54Nr3AtSf+jJqj3k8J5uUWgHoUphvMpWZkJpzFYrbaoTZIEW+9be9lHJfKvO8lug1z8cVlesAAJGYaqaXrYVfsJWJ7UtnM+8PR+ANiUGCoW77Ujm8SsQbd+lfF1lr9FJwp6OYjQL1pNpmhlRqvtOnv+2lvU1vMJJGdHE9BJ6HuW4KGn70CrxfPo3tT4zAjmfGY8dTzfB+91fNgvNqk28wUKsgcSnsK4tFqZxjTZVjAYMFdjaE4YaOlKUmojE1JWNygjW7in2LqlJqCu7BDjkqCcWUS5oIUFqbeekeTAYGVbbCHIms3QPG5AAgINK7FQAQ2CLWp9Sz27eEFPne4+fARZMj38WoF9gfaVOjtcIp66bMZM8Yee9Z+yAMZid61jyAHU+Nxs5nJ2JHjmrTUhrzQGmoPbKXmSg8OFJKkW+gNJSsxUIvu5vrpgIQS3BEg3vBdW8EAOyGqCQoD9uXzmZ+dyxzwmpk4SojFTEfa2KjJ/E1dmjXSQRKx3EgUS5zPcsyJRWI7b+3SSW6MDo8qDnmd+hd92hRg/Nqk++Y1ypIXC5jHiid+YZhjRDcYl3uqfZdsJsHrrERr+ioHOyNdIDkIKwg6L/GDnaG9o6MUJW2MprgS2pTk+AsK7Q7KsMwMFY0g+v8Skz/NlgQ7vwSYFjYRh+vxu0WhBT55gUx8j0sQWWhJBWkf4fEQimWs0zalEndpBORNmWsyTGgc6TAc+DDvag48AZ5QyuRa3dqvdJgU8FFeVn5oHdgJFXHToHnVCszAYi239kTLJH5pjTsXgz02sgbrNUwukcj0rsV4Y71CfUpy8dRWUprrKQirncVvsaWOrWxRka8AOwJhFHr0HeslUu3daC01tggF0VPUGyyUR5zvRm7+0IlMt8oE32wBgt61z+W8lrvukdQOesm1e9NbfINBkpBYkD8XfPt+t2f8io1UToBcL99Ahx7v8I0+66U1+ONdIYX87Y0QdrbcFEBPcGI3POCyA9yVBKKkZ02ZTHBl84hSu2F1eQWHZWRnh8QidWqtAw7pCSKc0uRb2lDmeiolFSHqZyVatUL7E97kZzzSjdlkrNRcshK0XeDtQbtr52W8r2VbmhLKRVTKv7NMkCNQ3+lTSq7q0kpbSjLpYYTkGx3QRCK6qQye6bFHJXr5IYJG8P1AMpljS29MV8OdjcZWFTbTdjj59Dep7+jkhwH+iCt82YDWxYHaXFP31cStpfFB1nWWD2C82pTyFlKiyBxKWTqFItSCgZ2W8bCAWCieWfK6xFvzFE5BBSVNpMBLosRfaEI2r2hsphftYRSvwlFCIKQEPnW33GgNdKGUu9uaYD6C6uxciwAsaGOv4TSviXSbeaLUS+wP8U8RBXSIEqNwuMllYoZG/N1TgsM7NBWOAFDOzBSykgb+XCUR29MXVQs5IY67V8gElNUfuMTD53lcIgqJXVZOaUDAiU235SR46Ck7B5Tznuc5iGvIgZKzPYKx3wxmvlpjWR3XzgKXyj3NVbNXgThCI89fvGcUB7n2NJZY1sNYiPXZnZryutSjUrjIO/4LVFKwo/BDjkqCUXsDXDgomKtBdpQFhe1U+6lhjrhzq8Q3PEeAMA+ppQclaltX4x6gf0pdrH5fDdlamxoS2nMl1PBc6C0It+7y6iZjhT5BnRoqFM3DQAQbv9CTv3+ITIMDAPUlYCKWGvkjXxfKYz5WLmDMhjzQGkp+8qloQsQr8FdGnYvn3keKK1GRu0Kx7wewXm1cVoMsBpjzQJ1bmQkrfFGlkG1rYzW2BIY8z9ERwEAPNHtEPiBDuuopKh0DO6O3xJxJ7H+th/sUOo3oQjJcVBpM8FiNOh8N9rjcZpR6zCjwqr/V0RttYexYgxYWy2ivjYwZhcMpkaYqier8t5q4HFaUOswI5qi0H8x6gVKCIIwaNQeSmpcZktXlsZ8g8sCXygCh45NJcpJ1QfEbW8x6B873O0tP6eNxcjKXZ+LhdT5m9vznfzYtkgDau1mGEtgHGiNNM9XWPVPixos87xaxNdYPvuTNYTnBfkQXQ62l+ze4LIgHOFhNur3PS/XMW8y6K8eVbq/0apOYzERmwVa4Oei6Avp61iVy9o4LWDLJFOn1mGGuwTOsZuDNfDzYudvrnszzNUTk65HYorKoZD6DcT39BGd19ihgP6jlxgUxIs/D/0oFABM8riw5da56PCGEY7w4HgejhSdyopBu8r1syyeGWi6aCOi/nYY7B5we74vqdSf644Ygyd+sj/2+rmUtte6XqBEbzCCUERcZEp9M6/GhtbAMthy61y0e8MwGlj4whHdxnw5Ne4CgB/v14ArZ49Gly/1mC8m5ZYG++RPpuLAkZXoCUaKanujowEGxzAIPAeDvQHhSBSBnVaMLRO7TxlWOmuspCIul8DIjUeNw7PnTEu7xhaLPYGwHJD0lIHtLQZWXmMFQNc1VmmdxKHCT/Yfhv89rLkk1thc9jfFDM5rxbPnTMOskZXoLfIa25/43qY8zrFTG90ls8a2+zhsjDRhqnkTuD3fDHBUxlO/h4ai8tZjJmBSvRPdAf3nm8EOWY1QRDkdXoNcFI9+vBXLPtqC7gCHSpsJV89pxk1Hj4PVVHw1qZq25yNB9K5/DL3rHk1wZl0FU82kktj4BLkoXv26Dct+v1V320vRV5fFCJsOf/dcKWRDG+SiWPze5tIZ8wmR76FOkIviz5/vxLKP9B/zQHmpWYNcFCs2deL0P3ymi+3rTvorLHVTY0Gjevz7+3a8+FW35p+rN0Euisc+3lY6802Z7W9e/aYNxz6h/3wjpdxX200wDXEVcZCLYvH7pbTGxpSsZTLPl8oaK9b7z832xQrOa0GQi+K9TZ04Q6c1NpFy29v87pPSWmM3cqKjMtz5NRzj4s0/BUGIp34PAUVlkIvitW/aMK8E1tihADkqCUWUS1dMXziCxe9txj3vfC8/1h3gcHfs/wuOGlv0qIhaiyvP+dDz2QPoXrUo/lioO/Z/RmzcokGdR6XEbb9RfkxP2w/Gw2s+G9pSHPPF6rauN6U25r2hCPycmAJdLra/9119bM9HgghufRvtr/5YDhodMPUqHHbq9Zp9ZilQivNNuTht5DFfIvNNuTgOSnHM0xqrj+27AxzC0cGRqVMoeq+x/SmXcgelON/s7gvhe24kAIDr+ibpGh/qhhAVsxoGe43KUltjhwJDO4RJqIa8oXTrr7rTEhPLYtlHW1JeW/rRFpjY4n9l1Co2z7Am9K57NOW13nWPgGH1rRVWarYvlyL/pWZ3IL6hJNvrM+YdZgOcOtYoLQZ62p7nfOhZ8xt0r/4V+FC3+FioG32rFyHwxRLwnE+zz9abUhvzQELzLjfNN8WkXBq6lJrdAbI9oI/tpb1NhdU45NVVpWb7eMo9nWOLiSAIaIspKgEg3PV10vWodxcAgLVWl0RmXyGUmu2HAmQxQhHlUqOyO8ihO5C64HN3gENPsLjFoMMRHntj91NoFJAPdcuH4tTXegp6/0IpNduXi9qj1OwOAG295RH5LjXbD0YVcb7oaftMQSPvev2DRlpSamPeH46gLyR2IaW5vsjzTZkonErN7gDtbwB9bF8uexug9GzfXiY1KkvN7n2hCIIRHt9HYorK7o0QovFu2HIjHcfgT/suNdsPBchRSSiiXNJEKq0mVNpSHxIrbaaidyiVGukYWQZVae5LKaylEqylMsO1ioLev1BKzfZynUQa80W+o/IpNVFqti8XuwP62r7Ug0ZaUnJjPlYn0WJkS6I7qpaUqu1pjdVjjS2PbuulZntaY2PXdJlvysP2pWp3r7EOjNkN8BFwe+Np6VHv0GmkU2q2HwqQo5JQRLksrhzP4+o5zSmvXT2nGRzPF/V+EpuKsGxhnbkFnoN72vyU19zT5kPg9Y30lJzty2RTU2p2j0R5dPnL4xBVarYvJ0WlnrYv9aCRlpTcmE/Y2zBMYWtsqVNqtm+nNVYXu4ciUVn1M9Tn+lKzPa2xIrrO9UPc9iVrd6cV5pp9ASSnf0d9Q6eRTqnZfigwtMPHREZ84QhMLIvuIIdKqwkcz6ct8irXcBritT0cZiNuOnocALGehFYdu5TafreKdRJZkwMVB94AQKxJGe/6PR8VB96ge22QYtg+lzEvqVmHet2yUhvzHb4wBAFgGaDWMbRTdErN9uV0iNJzvpGCRt2r7h3wGlcsaDSYOrvmQqmO+aFeqw8ovTW2XGoRl9qYl7pOmwyFZ+qUOqVm+3JxlgFx2wuAZt2n6Rw7kJIb8wlrrLlmX4RaP0lqqDOUUr+LZftyghyVZUqQi2Lxe5sVLR6CICQsrkPz8JSI1WTAgqPG4sajx2F3Xwj1Lgt4QVBtgsnF9m0qOw5YoxUVM69D5aybwId6wFoqIPCc7k5KCcn2C44aiw5vGMPcFkR4dWyfi92BhFpCQ1ztAcTtftPccWjrDcHjNEOIPa4Gudhe2tTUOS0wFKgiHgxItr/hqHFo94bQ4LIgqvd8UwZjHhBt/7+HNcvzTaPbCo7nizDfpA4aOaZehYqZC8CabAV/filTSmtsOTkOgBJbY2MNXcrB9vIae/Q4tPWF4HFaIECneb63fFTEQGmtsVK5g3IY84Bo+6vnNOOGoq+xye/PRXl0+SUVMZ1jCyXfc6ypZjKAfopKr6ioNDoHf+o3oO0aW46Qo7IM8YUjWPzeZtzzTrxGRHeAw92x/y84amxSVGRvgAMXFQCIKcjlgMNsxJ/X7sDi9zZj/2Fu/Pm8Gaq8b6621yL9mDU5AAAGex0AlJxyx2E2Yt7vP0FbXwgPnDIZ8yZ6Cn7PXO0OlN8B1mE2Yn1LD372ly8AAP+9/khV3jdX25ebswwQbX/tq1/h3Y2d+PnMJlx35FhV3jdX27eX2ZgHAJfFiOH3vIN6pwXLLz8EdSqMO2V2jweNoqEe8EYXVmxswzFsedjeYTbi2dXb8eAHP2DG8Ao8d+50Vd435/mmtzxqESfiMBtx9OMfo9MXxkM/3hdHj6sr+D3zWmPLzGnjMBuxdkc3LvzbOpgMDNb+8ghV3jfnfWUZzvMOsxH/+8qXeG9zFy6aNRLXHD5GlffNfU8fc86X0f7GaTGg6Z530eCy4IMrD0WlvfAzR757GwPLoNpWWmcerXCYjXjy021Y9tEWzGqqxFNnT1PlffM9x3qcFphrpdTvuKIyKisqh4ajEhBtf9gjH6EnGMEjp++Hw8fU6n1LgxaqUVmGmFgWyz7akvLa0o+2wMQmDwtpkqmwGssqIuC2mvBVWx82dfpUe8+cbV8mDV36Yzaw+KqtDzt7gqq8X652FwShbGpUJlJtN+Ortj581+6FIAiqvGe+8005RL0TsZuN+KqtD1v3+lV7z/xtXz5j3mhgIQjAV2198u9fKErtzpocYAxm/OCzYcSi/+D8FzfAaCifbZnbKo75TV36r7HlNM8DYtrvV2192KXTGsvzQjwwUka2L6k1tozsDohKp6/a+rCN1tiiYjEawEV5cY31hrO/QAH52t3jNBdc738wIa2xm7t0HPMJgRFztaiojHRvBh8JiP8eYopKCVPsHLurR519ZblSPjtiQqY7yMmFtAdcC3DoCSZfK8eFFYirR6XNtBrkavv2MnXaqG37XO3eF4ogGBGLHpeT7etiNSEjvJDWXrmS83xThodXQNxAA0CHSht5gGyvFMn27SrZPle7b98bQKcvXIZ2l+Z5/cZ8e7nvb/r0GfN7AxwifHll6gDxuSbA8fCFo6q8Z77zfLkFwKVxRmts8dF7T1+uzvmSOsc6LWDtHrC2WgACuD3fQRD4BEXl4K9RmYgWti9HyFFZhlRaTahMU0C70mZChTX5WrkUPO+P2odXIA/bl0nx5/7UqWz7fO3ushhhT1OYeyhiNRngtoq/r962ry+zMa/FpqYi7/mm3OZ6dW2f9xo7xBt39UeLMU/7G2XU6T3mY3NNtd0Es7F8jiIOixH2WGaS3rYvtzHvcUn7Sn3WWDFTR9xXlZ3tneraPvd5vlztXgprbMz2brEmrqSqDHd9Dd7fAQhRAAwM9nrV7rEUqFN5zJcr5bM7IGQ4nsfVc5pTXrt6TjM4nk96rNwjUX4uCl8oUvD7CYKA9a29mD97dMrrqWxfjvX6gMTItzoTfF8okp/dy2xTA6i/sdmyx5+T7cuxTiKgvt0DXBQfb92j2PbeUERW+JDtC6PdG8ptvimjxl2JSIfX3mAEQa5wdZkgCFjX0pOT7ct3f6OugjvvNbbM7A6oHwTf3OXLb8yX7Tyvjt394Qg+3qJ8je0JRhCOSpk6ZPtC2J3rGltGjbsSkeaaLj+HSJTP8uzsCIKAL3bltsa29SbXZTXF6lRynV8j4hPTvg12DxhDaufnYEWLjJFypHykQoSMw2zETUePAyDWk5A6ds2fPTpjx65ySxNxmA2wmVgEOB7t3jCaLYV9XR7+cAue+HQb/nPloWAYJqlb2i/mpLZ9ORY9B9SNfPtCEVz20nr87oz9AQCPrNyaNOZvPGocbObUnafrneWT9i3hcZqxqdOniu2Xb+zANa9+jRWXHwKGAZZ9tFXxfFNOKfeAuodXQRBwyYvr8cWuHnxw1aFgGSZprv9Fiu6M0pi3mwxwFjjXDTbUVHD/0OXD/zz/OV69aBaAgfNNpnm+3NbYSpsJRpZBhBfQ4QujqbKwbudL/vMDnlm9Pac1tlyDUmo6572hCC55cT2e+EmaNfbocbDR3kbG47Rg696AKrZ/a0M7rnvta7x3xaGK19jydc6rN+YFQcCFL6zDl219itdYyVnmLrN6/4C6Cu6NHV6c/9cv8Foua2xCQ5dyotpuBssAvAB0+sJocBeWqXTfik3409qditdYQRAGzPXmmlhDnT3fwjpE076BxGAgKSoLobxOI4SM1WTAgqPG4pa549ET5GA3G/D2hg6s3LoHc8cnd4As1w0lwzDwOC3YFttQNtfY836vN79rx/X//Bq8APzr29244aixuHXueHT5w3BbjfhgcxeM/Qo8c1Eee/xirY/yc9qoE4nieQEX/m0dXvmyDbt7Q3j1olm47ZgJ6AlycJiNeGtDO55evR3z+ymMy3XMA+rZfmOHF2f9cS32Bjgs+c9m3H7sBNw6dwL2Bjg4LQa8s6EDewMchtEhCkDc7l3+MCJRvqCmKr9esQl//WIXjCyDHzr98lzf7guhymbCF7t6KSiSgFoH2N4ghx89swZf7+7DVS//F0+fPQ23HTMB3UEOjtgau2r7XhwxNrkDZHuZjnlpjW3pDaLdGyrIUfnGN7tx4xvfQBCAtzd0DFhjP/yhC6Z+36kAF0VfLFui3Ma9WmOe5wWc/9cv8OrXbejyh/HKBQcOWGOfW7MDVxw6Oul15arqA9Sz/XftfTjnT2vRE4zg4Q9/wM1zxyevsd93oDcYobk+huw48IXB80JBTVXueWcj/v7fVpgMDLbuCcTXWG8IVXYT1rekWGPLdJ4H1AvEdgc4nPrMamzo8OHqf3yFJ87cf8Aau3ZnN2Y31yS9TvrcchvzBpZBrcOMdm8Y7d7CHJWvftWGW//9HQBgxaZOeY3t9IdRYTXiwx/2wNxvjfWGoghwMRWxU3JUiqnfXOfXiHhjjsoh1kgHIEWlWgya1O89e/bgvPPOg9vtRmVlJS6++GJ4vd6Mr3niiSdw5JFHwu12g2EYdHd3F+dmBwkOsxFmI4s6pwW//c8POOMPn2Hxe5sGPK9cD1FA/htKXziCcIRHuzeEUCSKCM9jQp0TF85qwvkzm2Tb19hNOPDBD3DS06vx6tdtSe8hfaaBZVBtK1dHZe4b+STbR3n8dMYITBnmwn0nT0KNwyyP+bc3tOOMP3yGXy3fiHAkdWpUuUVfgfzrqiTaPRzhsXWPH/UuCw4eVYW75k2Ux3y9y4KfPf85Tv/DZ3h05dYB71OuB9gahxkMAwiCmKaTC4m2D3JRTK53YR+PE8tO2w+zRlXJtuciPJoXLccRj63E1j3JXSDL1e5A/pHv/mP+y9Y+RAUBjW4rHvrxFLisYv09j9OC36zYFFtjNw94n3J1HAAJB9gcO673X2MFCJhY58RlB4/CT2cMl8d8lc2EA377H5z41Gq88e3upPeQxrzZwKLCWl5x+0IcB/3X2PNnNmHKMDd+c1LyGvvGt7vlNbZ/2qGsIi7HNdaVn+2TxzyP7XsDGOa2Yk5zNW4/dkLSGnvOn9bi9Oc+w2Mfbx3wPuXqMKtziL9vlBewN8dmgf3X2P0b3djH48Rjp++PmU2Vsu2D8hr7MXZ0B5LeQ6pPWZ7zfH7lnAausb1gGAYjKqx48NR9k9bYRe9uxBl/+Az3v59ijS3r/Y0651iWAfbxOHHV7NE4e1p8ja20GjFtyX9w4lOr8OaG9qT3kOZ5h9kARyxTxxRzVEb6toHb+z0AwOgYyo7Kws6x4QgPX7jw8nODlUGzMzvvvPPQ2tqKd955BxzH4cILL8Rll12Gv/zlL2lf4/f7cfzxx+P444/HzTffXMS7HXz87IARuOvtDXjn+05saPdioscpXyvXwttAfpv5IBfF4vc2J0ni588ejY/mz4bTbADDxKO4ZqMBp+0/DL9evgmPrtyKM/aPT9aJm8lCIr+DkcT6WblEvtPa/qo5cPU7iJ44qR4NLgva+kJ45atWnD1tuHwt3mChvBq6APlFAdPZ/YOrZkMQhAHKgvMOGIF/fL0bT366DbcfOx4Wo3g9EuXR6ZeKnpeX7Q0sg1q7GR2+MNq9IcUb6nS2//gXcwYUPG+ucWBaoxvvbuzE4x9vxW9OnixfK+95Xr0x/58rD0VbbwiNFcnj9/yZTbj33Y14c0M7NnX6MK7WIV8j26u3xrosxqQ11moy4Ef7DcP972/Goyu34NR9G+RriXZPfE05kHiIEgRB8e+fzfaJnLpvPeocZuzqCeLVr9tS7m/Ke8wrP8BmWmMZQF5DJc6bMQJvfNuOJz7dhlvmjpcbFoUiUdlJV27Nu8xGFpU2E7oDHNq9IdQ4lAkAclljx9U6sF+DC+9v7sLvP9mGe0/YR75Wro27AHXH/H+uPBQd3tAAdeD5M0fgvhWb8M9vdmPrHj9GV8ez4Gi+6VPF9h/Nnw13v3nebjbilH0b8NAHP+CxlVtx4qR4U5xUdjdYq2FwDEPU14rg9uXiY86hm/qdq6Myle2vTlFKolwYFIrKb7/9Fm+++SaeeuopHHTQQZgzZw6WLVuGF154AS0tLWlfd8011+Cmm27CwQcfXMS7HZyMrrbjlMni5NI/AluuNZyA3Ouq+MIR/HrFJtzzzvfojm0GuwMc7n13I5Z+uAUcLwx4zeWHjAbLAO9v7sLXbX3y4+Vaqw+IR74jvIDuoLLIdybbL/nP5gERKbORxWUHjwKAAcq+3WVs+1wj35nsvuyjLbCbBy6sP9q3ASMqrOjwhfHSf1vlxzt8YQgCwDJArcJDxFAi1818Jts/9MEPKaOwV80Wyxw8s3o7AgkNTNrKWEWspt0fWbkFY2oHlgkZV+vACft4IAjA47TGyuS6mc+2xoZTNAy44tDRYBjIgViJcrZ7XWx+DUZ4eEPKGhllXGPfH7jGWowGXHLwSADAY2nX2PKzfa6NjDLONx9tgdU08Ch3+pRhSYFYCSkgYDIwqErTuXcoo+Z8k36NHQ0AeGrVNoQiiWtsMHYP5TvmlQaksq2xo6oHrrH7eFw4ZnwtBAH43Sfbkq7JzXTI9lnJtsaGUqyxV8ZKe/x7Qzs2d/rkx9M1MTJJdSo7/wsAMAxhRaWY/l7YGnv3O9/jvhWbylJZOSgclZ988gkqKysxc+ZM+bFjjjkGLMti1apVqn5WKBRCb29v0k+5IE00f/hsB7yxuk2CIMRre5ThBC9t5pVuakwsi2UfbUl5belHW2BiB37lmipt+PF+osrj0ZXx15Zreg4gOhGlVDwtbX/ZwaNgZBl8tGUP1rf0yI/TIUoduy9LY3ejgcVlhwx0Ekt2r3WYYSgzFTGQe1OXfMb8yZPrMbLShi4/h7+tiwf6yjn9WE27L/toa0q7A/ED7LNrdsAf23DysUYyQHnO9XU5KirzGfOjq+04edLAQKw85svQ7g6LEfaYOkPLNVYKxL7XLxBbzvsbKRCrpd0TA7GPpVhjPc7yUxEDuSu487H9j/ZtwPAKK9q9yYHYck79zlX0kf8aKwZin161DcGYc4iL8nI5nXK0fa2Ktk835pMCsZ9slR+Xx3y/eV6qUylhHIKKSrfVKNfsVCr8yMf2Q51B8Ru3tbXB4/EkPWY0GlFdXY22trY0r8qPX//616ioqJB/mpqaVH3/UuaY8XWYUOdAbzCCP6/dCUD05EsKhXKc4OPqMmWbmu4gJ0dBBlwLcOhJow688lBxcf3T2p3oib2+nB0HQMKGsk872zdWWHH6FHGBTOUwK8dDVK4b+XzH/KUHjYLJwODTbXuxdmc3ABrzuSr78rG9gWVw+aGSk3gLBEFUeVMtYqAvFFEU+c53zB8/0YMxNXZ0Bzj85YtdAMTmSdGY0l5ymJYTuSq487W95CRODMTKTpuynW9yC0rlY/umSht+FEu3T+kkLkPbF8PuQDwQ+2FCILac9zZAcWxvNLD4f4cMdBK3UwAce/wcuBSKvP7kO+ZTBWKl85uBZVBjL8c1NrdAbP7n2NEAgGdWxwOxci1iV39H5b5J/x+KXb/FZoHFsf1QRldH5U033QSGYTL+fPfdd0W9p5tvvhk9PT3yz44dO4r6+XrCsozcmfHRlVshCII8ybitxrKsjeBx5bapqbSaBtSska/ZTKiwpr521LgaTPI44QtH8ceYk7icG7oAuW8o87W9dIB9/vOd2OsPJ4378txQ5uYsy9fu9S4LfhKrWSY5icv9EJVrI6N8bX/xrJGwGFms3dmD1du7ASQ6DspvI19hNcJkENVFShxm+dqdZRlcccho2luWcwAAWh9JREFUAAlrbGzM19hNA7pSlwPFmuePGV+H8bXJgdhyLvEB5B6Uyn+NlQKxO9Ab5GKZOrTGam33VIHYct7bALkruPO1vRSI/WTbXnzePxBbhvubarsZUpJMpy+77fO1e/9ALBC3e53DXHb1/oHcg4H52v74fQYGYtPt6aXUb9ZWC1PNfjA6hmMoUqzz1FBG113xddddh2+//Tbjz5gxY9DQ0ID29uROUpFIBHv27EFDQ0Oad88Pi8UCt9ud9FNO/HxmExxmA77e3YcPfugq6wLEQMIEr2BhBQCO53H1nOaU166e0wyOTx1JZBgGV8YcZo/FVE7SRqrcCp5LFMv2c5qrMWWYCwGOx3Of7YjVE4mpiMtwQ5kY+e7fqTUV+dodiDuJX/hiF7p8YZpvclRw52v7OqcFZ08VncSSyilu+/JqYgRIkW/l800hY/7CWU2wGlmsb+nFx1v3lnXjLqB48zzLJqyxH29NcpaR7ZUdovK1vRSI9Yai+ONnO7E3wIGLCrF7KD8nsWT3Tp/YLDAbhcw3ksrpL5/vwl5/uKzL2gC5O23ytX1SIHbAGlt+tjewjFx3XMn+ppAxf/GskTAbWHy2swert+8ta7sDCTVxNV5jDSyDy2OB2MdW9l9jByoqPae8jKaLNqL+1P8Da60Ez/n6v+WgJ1dFZSHjfqiiq6Oyrq4O++yzT8Yfs9mMQw45BN3d3Vi7dq382hUrVoDneRx00EE6/gZDj0qbCefNGAFAjMC2lbnCKddoiMNsxE1Hj8Ptx46XoyKVNhPuOHYCbjp6HBxmY9rXnn9AE1wWIzZ0+LB8Yyfaesu3+DOQe00byfa3HZOb7RmGkRUfj63cipaY3Z0WAxyW9H+voUquke/4mJ+Q85g/eFQVpg93Ixjh8czq7fGGLuW+ocx1zOcx30hj/m/rWtDeFyrrxiJAwoayL7vtJbvfkceYr7ab8VN5jd1Cdi/iGvvzmU2wmwz4qk0MxJb7/qbOldshqpA1NtFJ3Nor2r3KZhrQrbockBw2UV6QO3BnopA19rAxYiDWz0Xx3Gc7aL7JsZFRIWusNOb/+rkYiCXbK5/rC1lj65wWnDMtnq1Ddi/eGntRLBC7LhaITWt71oDw7rXY8VQzdj47AdufGIGez5aAjwTz+A1Ll3xtn+saO5QZFL/xpEmTcPzxx+PSSy/F7373O3Ach/nz5+Occ85BY6M4Ge3atQtz587FH//4R8yaNQuAWNuyra0NmzZtAgB8+eWXcLlcGDlyJKqrq3X7fUqdq2aPxhOfbsMrX7WhOdZZrXwn+PimhucFRWkDVpMBZ+7fiBuOGoe9fg4epwUcz2dNnXdZjfjZASPw2Mdb8djHW8s+RSfXSBQg2v6Q0VW48ehx8IaiqLKZFNn+vOnDcePr32Bzlx9/iqUFluvhVarj0+ELo90bRoM7u9rIajLgwgObcMNRY9Hl49DgUjbmJSfxJS+ux+8+2YaDR1UBKF/b55oSCIi2P26CBzceNQ49wQhq7WZFtj9wZCUObKrEmh3dWPrRFvjCYm1Gsr0y21tNBlx9WDMWHDUWHd4wGt1WRXYHxDX2mdXb8dJ/WzGi0gagfOf5xEZGgiAoavBhNRlwxpRhOa+xlTYT/ueAEXji0214bOVWSv3O8RAFiLY/eFTua+zPDhiBm//1Lb5r9+KvsbTAch3zZiOLKpsJewMc2r0h1Diyjz+ryYALZua3xl556Ghc8fKXePzjbZjeKGaJles8n2sjI0C0/bHj63DjUePQG4ygRuEae0gsEPvFrl48+MEPZV3vH5Dmmz7FtreaDJg/J7819srZo/HHtTvxt3Ut8hpTrmM+33n+NGmNDXDwOJTNN9V2M86dMRzPrt4hnmNTOCp5zoeezx5A9+pfxR8LdaN71b0AgIqZ14E1ORTfaylTm2NpG0C0/ayRVdiR4xo7VBk0BZGef/557LPPPpg7dy5OPPFEzJkzB0888YR8neM4bNiwAX6/X37sd7/7HaZPn45LL70UAHD44Ydj+vTpeO2114p+/4OJKcPcOHxMNaK8gBfXt2C/BhfGxByW5Ya0qYnwArpzKGL76MqtaF60HK9+3QazkVUcBZFSYV/7ug29wQj2a3ChUYGjaCiSa4oOAESiPE55ejWaFy0Hy0Cx7R0WIy44UGyc9fznO7FfgwsT64bGQpkP+Wxs3trQjuZFy/HA+5tyGvPnTGtElc2ELXv8+HjrHuzX4MLIKlte9z3YycfuAHD1P75E86Ll2NThy2u+WfrRD6h1mDFzRAWclvLcDOVj+02dPjQvWo5L/74+J7tPH16BQ0dXIcIL+HtsjW2uLs8xXxdz0oQiPPpiTW6U8PBHW9C8aDne+HZ3XmP+/75qg5+Llvkam5u6DADCER4nx9ZYk4FRbHu31YTzD0heYyfUlvMam/sB9o1vd6N50XI89MHmnMb8eTNGoMJqxKZOH1bv6C7zNTZ3uwPAVf8nrrFbupSvsaKTWMxckNbYg0ZWwlamDod8xAcb2r1oXrQcV7yU2xo7a2QVDmyqRDjK4+X/toprbE15nmMlu3tDUbnJjRIe/M8PaF60HG9915HbGhsrN/H39S0IRXjs1+DCsARHJcOa0Lvu0ZSv7V33CBh26NRhzLWcEwD4wxGc+oy4xtpMbE62H4oMmt+8uroaf/nLX9JeHz16tNy9VOKuu+7CXXfdpfGdDU1uOno8fnk4j2Mm1IqqKpcFvnCk7L4sZiOLSpsJ3bHId7XCjnEtPUF0+sI5N0eYVO/ChQc24dR9G2TbN7qtZWn7fBwH7d4weAHYG+AU/60k/vewZhw5tla2e72zPMc8IG5svt6dm+1bekPo9IXBKai5lYg9luowoc4p236Yuzxtn89GHgB2xeYbtzU3e501tRG//2QbbjhqHI6ZUIsObxhcVADHR8vO9nV52L6lV7S7NwcHm8TNR49HhBfKfo11WIxwmA3whaNo94bhVlgsvjU25s05rrFThrnxPwcMxxlTGmmNzWONbY2VRukJcqhKU/Q/Hb88fAyOnVBHayxE22/o8OU130RyW2LhtBhxw1HjMLneRWtsHlkLgGj7PX4OFbbc9pXnTm/EU6u24aajx8trbDjCg+P5srN9ruWcgIQ1NpbxkQu3zB0HgCn7NdZlMcJiZBGK8OjwhjGqWtnvL9neYsxtjZ0xohLnTm/EWVOHy7YfnrDG8qFu8KHulK8Vr/XAYK/L6TNLlXzW2JZYaRR/OApXGZYf6w9ZgEjJEWNr8OvlG3Hh39ahO8Ch0mbC1XOacdPR48pOfuxxmkVHZV8Y+3iUvUaqc5iPUuPhH++Hxe9tKnvb5+O02dUj2r3BZYEhx+5+DS4rntm5o+ztDsRrROZj+3zG/FWzm3HfCppvpE1NXyiCABdVpLzgorz8d8rV9laTAf+65CAs+c9msn0eCu5CxvzR42tpjY3hcVqwZY8f7d4QxilU2e2S1tiK3G3/6On7435aY/M8RMXHvJI0/USGV1jxh89ojQXytL083+Sewvq/hzXjvhU05j2xMg97AxzCER5mBU6YABfFHr+YUZWr7e1mI9689GBaY1HYnj6fNXbeRA+tsZCaBZqxozuIdm8YoxRmSBZi+9+dOTXtGmuxVIK1VKZ0VrKWSrCWipw/r1TJR8G9qycAQNzb5LrGDkUGTeo3UTx84QjuW7EJ9767Ed2xQt/dAQ53v/M97luxCb4cpONDgXw2lNIhaniOhyhfOIL7399Mtke+dhcn+OE5Lqy+cAS/pjEvk1fkO7a45jPmf/Me2R4A3FajrBBT6jCTFE4mQ7yrplJ84Qh++8EPZHvkOd/05OcsozU2mfw287E1No+5/gFaYwEU5jjIZ56nNTZOPgruQvaVv3mPxjwAVNtyaxYIxB3ENhMrN7hQCq2xcQoKBtIaWxCF7G/ymW8yrbGRSBjuafNTvtY9bT4EXnmZtVInHwW3pKjMdW8zVCFHJTEAE8ti2UdbUl5b+tEWmNjyGja5buZFhVN+Ew3ZPo4U+d7j58DFipBno6VHtHuumxqyezL5HGDlxZVsnzdS5BtQbnvJ7o1uq6JmX4mQ7ePkM+Zb81TOk92TyXUzH0xQONF8kz+JjgNeYckOSVFJe5vCKExRSbbPF5Zlcg7EymO+wpazwolsH6eQUhPDK3KrqUp2TyZX2/cFI3LNaLXnG8FgR8WBN6DyoNvAWioBiErKyoNuQ8WBNwyZRjpAchC2f3nCdOTrIB6qlNc3lVBEd5CToyADrgU49OTQVGYokOumpq0vBEHIT+FEto+TT+RbUlTmurCS3ZPJL/JNtleDXDeU+dodINsnUkzFAdk9mXwdB1Zj7gonsn0caX/CC8CegMI1Njbmh9GYL4i81tg8FZVk+2RyVXDvKiDlnmwfp7DU79xsT3ZPJvcAuGh3l8UIV461z5XYnjVaUTHzOoy8bCdGXrYLIy/bKXb7Ng4t55y0t+GiAnqCylS8ku2HkaISADkqiRRUWk1pN/+VNhMqFBa7Hyrk7jiITTKu3BVOZPs4+US+W3vyU/WR3ZPJdSMfikTRlafCiWyfTK4bykKir2T7OIl2Vxr5bsnTcUB2TybfQ9TwPGo4ke3jmAwsqu3i79vel6PtczxEkd2TyXXMe0MR9MYOusPduanLyPbJ5KrgbslT1QeQ7RMppCYurbGFkb+KWLt9JWtygDGYYbDXgTGYh5SSUsJmMsgNcRTbnhSVSZCjkhgAx/O4ek5zymtXz2kGxytLwx0qSBvKjhw3NfkU+SfbJ5Oz0yZPRSXZPZmcN/IxB7HFyObcCZZsn0yum3kp9Tuf6CvZPo60kQ9HedkhkI18i82T3ZPJVV1WSJF/sn0yOc83edaMI7snk/s8L9rdaTHkrHAi2yeTt/ggD0Ul2T6OZHdfOApfKPsaKwgCrbEqQWusfuSu4M4/S2ooQl2/iQE4zEbcdPQ4AGI9iXLulgbkv6nJpxAu2T4Z0fZ9uds+x0MU2T2ZnMe81MQoD4UT2T6ZnCPfeTYxAsj2iUiR775QBO3eECqyONx9oYicykPzTWHIG/k+bed5gGzfH4/TjO/acwkG5re/IbsnI9Xgzlk5T/vKgsm1kVEh6jKyfRynxQCrkUUwwqPDF4bDktkF0ROMwM9FAdAaWyjFzNQh2yfjcVqwucufc915UlSKkKOSSInVZMCCo8bilrnj0RPkUGE1geP5sptggALq2eQ5yZDt4+SrLstngie7x5HGvBT5zrahlBSV+XapI9vHyVXBXWjhbbJ9HI/THHNUhjG+LvNzExVO7jzSyMjucfJNxaQ1tnByWWMTFU60xhaGZPfuAIdwhIfZmDnBrZD0Y4Bsn0i+KuJcU+4lyPYiYrNAC7Z3B9DuDWF0tT3j8yW7V9pMsJtzd1eQ3ePkq+CmNbZwcgnECoJQUGBkKEKOSiItjtjCICl8zGVaKSDXQ1S+nWATIduL5BL59ocjcgHnfG1PdhdxWYywGFmEFEa+5ZT7AhZWsr1I3htKmm8KJh75zm77QlKjJMjuInmnH5PtCyYXBXdfKAJfWFQ40RpbGJVWE4wsgwgvoMMXyuqALKShiwTZXiQeDFSaMSI5bcj2heJxmmOOyux7etnuNOYLJuc60LTGqkZdDj6EPX4OoYiYGp9PqYmhSHmOGoLIAekQtTcW+c5GoQonIk4uB1jJ7g6zAe4cazgRyYiRb+VKYjWcNoRIzgpuir6qRk5jPs8UWGIgkt07/WFE+eyNjGiNVY9cDrCS3SusxqzBKyIzYrPA3G3fmKeikoiTi/ggSUWcp6KSiJPPnp7m+cJJtLuSZoG0r1SPfMZ8rcMMi7H81KepIEclQWShymaCIda9u9OXfWNDsm31kCPfCjqSJirLcq2TSAwkl818K9VUUQ2PS7nd+4IReEOFKZyIOLlEvvNtKkIMpNYhzvOCAHTRGltUcmmyQHZXl1wOsK0qqMsIkVzsvjcQVzgVoqgkRHKxvTzfkIO4YKSgSIQX5MyzTKihqCREclFw7yqg5vxQhRyVBJEFlmXkgxSpy4pLnYOir3qRWxSQutSpRV3CXJMt8i2l3LutRjhJ4VQwdXkoKmnMF47RwKLGLtb5zGb7QjrBEgOpo72NbuRje9rfFE6icj7rGhuze43dRAonFajNQ0U8jBzEBWMxxjPNstme56lOoprIwUBFQVhxLaA1Ng45KglCAUpTAvuCEfSFxE6wNNEUTi6pmNQpTV3I9vogqfq4qCB3lU5HIZ1giYHE1WUKVMTkOFAVpQru7gCHoKRwonFfMLko58lZpi6y7XPIGMm3mQ4RR7J7gOPlmqvpILurSy4K7lZSVKqKUvFBpy+MCC+AYYAGFzmJCyWf1G/K1IlDjkqCUIDSzby0qXFZjHBRncSCye0QJarLhtHhVRWUpsGKCidSVKqFzWSAyyJFvjNvbArtzEgkk9OGkhSVqqLU9tJGvtpuKssOomrjceUSkIopnGjMq4JSBXeiwolSvwvHYTbAZhKPv0rnG7K7OuRV+5wUlaqg1PbS3sbjtMBkIDdRoeRSB1qN5phDDRqBBKGAuHRb2aaGFAfqINndz0XhC2VWl7WQ7VVFSkvrzLKp6Q5wCHBSDSeyvRrEa9pk3tiQolJdlNodoLlebSSnTbb0KCryry7SGtsTjGRtFkhrrLooTQns9IXBRcUUZXISFw7DMHJZIaVrLDUxUodcshaoiZG6KLU97SvVRRJ9dPnDiERpjc0VclQShAKUdmeUasbRJKMOTosBVqM4TWXbzFMkSl2Uq4hFR2a13QQbKZxUIVd1GTmI1UGp3QVBoPlGZeoU2r6FDlGqUmk1wRhrFkiB2OKiNA22RVY4mUnhpBJK1WVUq09dlKrLoryAtj6yvZooPcfS3kZdauwmMEysWaA/cyMj2YdAtpehFY8gFKD0ANvSQ4Vw1YRhmJydNrSpUQfFaSKU9q06Sm3fShtKVZHmmk5/GFE+fZMFUjipT65pacNonlcFlmXoAKsTSp02tLdRH8WBWEr9VpXE/XymRka7+0LgBYBl4t8TojAoAK4PYrNApYGRmA+BbC9DjkqCUICcEpil6PkuqhmnOko286LCKdbQhQ5RquBx5aaopEOUeiitD0oHWHVJinxnUHAnKpzMRtpGqYHStDRSVKqPkgNslBfQ2kdzvZooDoBTUxHVydn2lPqtClJQJMIL6A6kV5dJdm9wWWEkFbEq5KrgpnlePZQEYrkoL1+n/U0c+vYThAKUbmpa6RClOkps3+UPIxyr/UEKJ3VQGvmWFZW0qVGN3A9RZHs1UBr5jjdYILurBaVi6ocS23d4Q4jyAlgGqHeSukwNlK+xkoqY7K4WShsZUTBQXSxGAyqsUrPA9EEpsrv6KFVwt9D+RnWUKLjb+kIQBMBkYFDrIBWxBDkqCUIBiiXzpKhUHSW2lzY1dQ5SOKmF1EwnW+SbnDbqo6SpC88LaO2lUhNqo2QzT4co9VGaiknzjfrIts+QMSLtbepdFlI4qYQ01wQ4Hr5wNO3zdpGiUnWUKLi5KI/dXlpj1SaXPT2l3KsHldHSj1zG/DCXFWysbjRBjkqCUITyWkJUCFdtlNTPaqGaKqpjNRngVhD5bpUPUWR7tVCyqWn3hhDhBTAM0OCizbxaKLG9pOoj9bZ6kIpYP5Q0MqJOsOrjsBhhjzWgy2T7Vnl/Q/O8Wnhc2RWVu2MKJyPLyIFbonCUKLjleriUcq8aua6x5JxXDyUKbtl/QHubJMhRSRAKkCZ4PxeFLxRJ+ZwkhRNNNKqhpK7KLnKWaUJOkW8a86qRi7Os3kkKJzXJZczThlI9pMNrbzCCIJdaXRaJ8tjdRwontVGi4KZgoDYoUnDT/kZ1lCi4ZYWT20IKJxVRYntqYqQ+0lzT5ecQiZXK6k8oEkVnrD437W/UQ9GYpwyplNDphiAU4DAbYDOJX5d0E02HL0wKJw1QEvkmZ5k2KIl87yKFk+pQ+rF+5KLgJturR6XNBGPMGdCRppFRW6wTrJFl5I0/UTiKnPOkstGE3AIjpC5Ti1zGPNldXRQpuHsldRnZXi2q7WZI/vbONGtsS4/4N7EYWVTbTcW6tSFPPBhI59hcIUclQSiAYZisGxtJtl3vtMBECifVUBaJokOUFmSzfaLCidQe6iHZvcsfThv5JkelNuSiZqX5Rj2UrLHxlHtSOKkJqYj1I5vtExVOlPqtHrLjwBcGz6duZNRC5Q40QUkgVnKY0XyjHgY23qQlne0T9zYMQ2usWig5x7aS6CMl5E0hCIVkP0SRbFsLFDkO6BClCdnqquz2igonA8vIUXKicGocZjAMIAhimk4qqE6iNuQS+ab5Rl3kA2xfumAgOYi1QInjoJWc85pQ58pse6mckNnAosZOdRLVos4h7leivIC9aZoFSorKYTTPq0ou5ZxovlGX7IIb2ttoQW4NpMj2iZCjkiAUkm0zT4VwtSGxflbayDdtajQhWxRQinoPc1lgIIWTahhYBrX2zE5iSrnXhmxjPhzh5dRkmm/UJet8Q2NeExIPUYKQeo2lA6w2KFURN7otpHBSEbORRaVNTG1NZ/tWUlRqQrYx7w9H0B1zHtN8oy7K5xuyu5ooKqNFPoSUkKOSIBSSra6KpKgkhZO6SJHvCC+gO5gm8k2HKE3IFvmW6gjRpkZ9sm0oWyn6qglZ7R7byJsNrJxGRahDts18vLkFjXk1kToaByM8vKHUjYzoAKsN2RoZ0d5GO7LON3LnacoWUZNsog/pLGUzsaiwGot2X+VAdsEN1UnUAmlf6Q1F4Q+nbshLWZmpIUclQSiEJPP6kC3yHY7w8qJLtlcXpY4Dsrv6ZN1QkrpME5TanRRO6lOXVcFNY14LHBYjHGYDgNRzfYCLYo+fFE5aoHxfSU1F1Cabglu2vZtsryZK6/0Pr7DRGqsy2QU3pCLWArfVCHOsd0WqoFRfMIK+kOjApDU2GXJUEoRCskW+W0i2rRnx2mUDbd/WJy6sJgNDNZxURmkqJkVf1cfjUniApQ2lqkhjvi8UQYAbqC5rIcWBZmRTcFPqt3Zkch5IY95migcNCXXIpYEUoS7ZArE032iDJ1aXdY+fA5eiWaCkLKO9jfpkVbPS/kYTxGaB6W0vzTVuqxFOC6mIEyFrqEQ0GgXHpU5LJYYGw50sRrmMECIhBIPBAdf5SBijXEY0OQ0prw9FTCYTDAaD5p9T5zDj+w5fyg1lYgFi6gSrLlkPUZR+rBnx7owDbR9MUDjRhlJd3FYjTAYGXFRAhzeEkVX2pOu7SHGgGUoV3DTfqE+dw4wte/wZD1HUCVZ96rJ14SVFpWZkUnB7QxH0BkWFE8036lJtM4NlAF4AOn3hAaU8KP1YO+qylnOi/Y1WeJwW7OwJZj3HEsmQo7JABEFAW1sburu79b4VQmPGm6J4/FgPzAYWW7ZsGXD92mlO8IIDHr4bW7b06nCH+lBZWYmGhgZNDzCZlH0U9dYOyXEgRb5NhmQRPtleO5SMeauRRRUpnFRFjHxbsKsniHZveICjkhQH2kHNdPQjU1CKSnxoR6KKmOeFAcFWGvPakVFFHLO7y2KEi+okqgrLMqh1mNHuDaPdGxrgqKR6uNqRKRgoCAKVV9GQTLaX6v2T3QdCs2+BSE5Kj8cDu91O0eYhTCAcAd/lh9HAoNnjSrrG8wJ81j4AwFiPE0bD0K+qIAgC/H4/2tvbAQDDhg3T7LMy1VWhSJR2VNuVRb5pcVWfeKmJzI4DWnPUx+M0xxyV6Q+wNN+oTybHgS8UQU9M4UQ149SnzpXhEEWqPs2QlPO8AOwJhFHrSE7xju9vKPVbbZSusYT6eJyWmKMyRSCWbK8ZmYKBPcEI/LFyNxSIVR85KOVLNeap3EE6yFFZANFoVHZS1tTU6H07hMawRh7oiSDKABZLciOFUCQKGM1gGMBhL58C0DabeHBpb2+Hx+PRLA08YySKFE6aYWDFup8dvtSRb3ISa4cSRSXZXRsy2Z4OsNpRl1DDSRCEpHVUSklzWgykcNIAJfMN1UlUH3NMFb83wKG9L9lRKQgCOYk1pM6RfZ6nNVYbxPmmL82eXlSXke3VR4lyvspmgs2kfUmvcqM2o6IytsbSvnIAQ1/2pSFSTUq73Z7lmcRQwGgQD02CAER5IelaOCr+32xgy8ZJKSGNfy1rtEqLa2eKSFQrOW00JZ6almz7JIUTLa6qk6mxCDnLtCWT7clJrB1Svb5wlJc7YEq0UPMoTZGCganWWEo/1hZZ2edLnm96ExVO5CRWnUyKShrz2pKpOancTIdsrzqS3X3hKPzhfmss7W00Jd1ZCgBaaX+TFnJUqkC5OabKFZZhYIjVD+L6OSqlznX9a/iVA8UY/1Q/Sz/SqVmlTY3DbICLutSpTqYOgdKY769wJdShLo3tkxVOZHu1cViMcJhFJUd/2++iQ5SmKFljyfbakE7NKtm90maC3UxrrNooUc7TGqsN6co5CYJADjMNcVmMsBjFc2p/hxntbbQl4xorjXmy/QDKz6tCEAVgijkqI9HUjkqzgZzWWpDJadNCXeo0xeNKvZlPVBxQsEZ9pE1NXyiCQExVI9FKag9NSaeo7AtF4AtLCieyvRak28xT3TJtUdJYhGyvDWnHPO1tNMUTq8u6N8AhHOGTrtEaqy3p9vR7/BxCsb9FYwWpiNVGbBaY2vYt5CzTlMwlzGLNdGiuHwA5KgkiB6QmORyfvKmRUr/LUVFZDCgSpR/pIt+7KFVBU9xWI8wGKfKdxvY05jUh3Xwj2b3CaoSDVMSakG4zH5/nqVafFqQ7vCapiKmJkSakU3DH62+Tw0YLqm1is0BgYMkDWmO1JV0wULJ7rcMMi5HqJGpBtv0NBWG1IZ2Cm+cFtFK5g7SQV4UYktx1112YNm2a6u8rKyrTpn6TskwLpMj3Hj8n2xoAeoMcvCFSOGlJugMsNTHSFkWRbxrzmpBtzNNmUjvSbeZbqPuxpiQ6DviE/c3eQFzhRM10tCGt46CXHMRawrJM1kAsrbHakN5ZRo10tCZ91kJM1Uf7G01IDMIKQnyN7fCFEeEFMAxQ76I1tj/kqCwBfOEIwhEe7d4QwhEevn4FbonSQWqoMzD1O95MZ6jw/vvvg2EYdHd3630raSPfksPGbTXCSQonTUgX+SZnmfak2lBSnUTtyZaKSWNeO9I5Dij9WFtqY42MeAHYE4ivsdJcU2M3wUqdYDUh7Ror1UkkRaVmpFJw87yA1j6ab7QkfRCWlGVak832tL/RBmlvw0UFuREpEN/b1DstlJWZArKIzgS5KBa/txkNC99Gw11vo2Hh27j/vc0I9qtJRpQGRjZd6nf5NtMpBuki35R+rD1pm+mQs0xzUm0o9wY4BKUaTjTuNSHR7omRb3IQa092NSupy7TAZGBRbTcBANr7Bjoqye7akU05T4pK7Uil4O70hcFFRYVTAymcNCFr+jGtsZqRtZwT2V4TbKZ449GU51iye0rIq6IigiDAF4oo/ukNcvj1ik24553v0R3gAADdAQ53v/M97luxCb1BTvF7JR6msvHSSy9hypQpsNlsqKmpwTHHHAOfzydff+qppzBp0iRYrVbss88+eOyxx5Jef+ONN2LChAmw2+0YM2YMbr/9dnAcJ19fv349jjrqKLhcLrjdbhxwwAH47LPP5Osvv/wy9t13X1gsFowePRpLlixJev/Ro0fjV7/6FS666CK4XC6MHDkSTzzxRE73oISvv/4aJ598MtxuN1wuFw477DBs3rwZAMDzPO6++26MGDECFosF06ZNw5tvvimndm/dshUMw+DFF1/EYYcdhgOb63H+yUdj66aNWLNmDWbOnAmn04kTTjgBHR0d8mdecMEF+PGPf4yFCxeirq4Obrcbl19+OcLh+CYpFArh6quvhsfjgdVqxZw5c7BmzRr5uqR0XL58OWbOnAm73Y5DDz0UGzZsSPr9Xn31VcyYMQNWqxVjxozBwoULEYnEozgMw+Cpp57CaaedBrvdjvHjx+O1114Tf7+tW3HUUUcBAKqqqsAwDC644IKc7Ks2qTbzNMFrT9qOpKQu05xUm3nJQVxNCifNkDby4SiP3oTINx2itCeVuoznEzvBkuNAK1LON2R3zcnmtKH9jXaksr20t/GQwkkzJLv7wlH4QglrbC+lfmtNqjU2EuXRRipizUkl/KByB5mhXEkV8YejcN36b0XPrXWYseXWuVj20ZaU15d+tAULjhqL5kXLBxR5TkXfohMUFfdvbW3Fueeei8WLF+O0005DX18fPvzwQ9nR+fzzz+OOO+7AI488gunTp+OLL77ApZdeCofDgZ///OcAAJfLheeeew6NjY348ssvcemll8LlcuGGG24AAJx33nmYPn06Hn/8cRgMBqxbtw4mkxipX7t2Lc466yzcddddOPvss/Hxxx/jyiuvRE1NTZIjbMmSJbjnnntwyy234KWXXsIVV1yBI444AhMnTlR0D9nYtWsXDj/8cBx55JFYsWIF3G43Vq5cKTvyHn74YSxZsgS///3vMX36dDzzzDM49dRTsfKzL8BUDZdrVN55551YsuS38Fmrcc+CX+DCn/8MLpcLDz/8MOx2O8466yzccccdePzxx+XPXr58OaxWK95//31s3boVF154IWpqarBo0SIAwA033ICXX34Zf/jDHzBq1CgsXrwYxx13HDZt2oTq6mr5fW699VYsWbIEdXV1uPzyy3HRRRdh5cqVAIAPP/wQ559/PpYuXSo7YC+77DL5niUWLlyIxYsX4/7778eyZctw3nnnYdu2bWhqasLLL7+MM844Axs2bIDb7YbNpm9UX1xc+9IcomiC1wrqwqsfKVXElAKrOVLkuy8UQbs3hAqbuH610nyjOfJGvi8+5rv8Ybm8yjCyvWZ4nGZ81546GEhNjLRDqsFNtYiLT6pGRi2UqaM5TosBViOLYIRHhy8sn19beyj1W2tSiT7avWHwAmBgGXnPT6iPx2nB5i5/8nwjpdzTmE8JhYp0osFlQbs3LCsp+9Md4NDhDauedtDa2opIJILTTz8do0ePxpQpU3DllVfC6XQCkBxvS3D66aejubkZp59+On75y1/i97//vfwet912Gw499FCMHj0ap5xyCq6//nq8+OKL8vXt27fjmGOOwT777IPx48fjJz/5CaZOnQoA+O1vf4u5c+fi9ttvx4QJE3DBBRdg/vz5uP/++5Pu88QTT8SVV16JcePG4cYbb0RtbS3ee+89xfeQjUcffRQVFRV44YUXMHPmTEyYMAEXXnih7Ah94IEHcOONN+Kcc87BxIkT8Zvf/AbTpk3DE489AgCIxFK/r7/+ehx1zDw0j5+In158OdauXYvbb78ds2fPxvTp03HxxRcn3TcAmM1mPPPMM9h3331x0kkn4e6778bSpUvB8zx8Ph8ef/xx3H///TjhhBMwefJkPPnkk7DZbHj66aeT3mfRokU44ogjMHnyZNx00034+OOPEQyKG6yFCxfipptuws9//nOMGTMGxx57LO65556kvyMgKjzPPfdcjBs3Dr/61a/g9XqxevVqGAwG2Snq8XjQ0NCAiooKxfbVgpSRb1I4aY60qUmMfAuCQPVsioBk+45UjgOyu6aQglsfUim4Jbt7nGZSOGlIxjWWFJWaIdm9O8AhHCvrEeUFtPWR00ZrMikqaY3VDrFZYCrbk7pMazLN8w0uCwwsNYXVilSBWNrTZ4YUlSpiNxvQt+gExc83GVhU2kwpnZWVNhMa3VZ88os5ij9bCVOnTsXcuXMxZcoUHHfccZg3bx7OPPNMVFVVwefzYfPmzbj44otx6aWXyq+JRCJJTqq//e1vWLp0KTZv3gyv14tIJAK32y1fv/baa3HJJZfgT3/6E4455hj85Cc/wdixYwEA3377LX70ox8l3dPs2bPx0EMPIRqNwmAQf4/9999fvs4wDBoaGtDe3q74HrKxbt06HHbYYbLSM5He3l60tLRg9uzZA+7zi3XrAIibSOk+wzGnZX19PQBgypQp8mvq6+uT7hsQ/wZ2u13+/yGHHAKv14sdO3agp6cHHMclfbbJZMKsWbPw7bffJr1Poo2GDRsGAGhvb8fIkSOxfv16rFy5UlZpAkA0GkUwGITf75c/P/E9HA4H3G73gPstFVJFvknhpD0uixEWI4tQQuS7yx+W67KSwkk7MqZi0uFVU+KRb1JwF5NMY54cNtqSSsHdSrbXnEqrCUaWQYQX0OELYXiFDe3eEKK8AJaJH24J9YkHAweWV6E1Vls8TjO2dwdSqstovtGOVEFY2tsUh7oUgVhaYzNDoWkVYRgGDotR8Q/H87h6TnPK97p6TjM4nlf8XgyjLAJiMBjwzjvv4N///jcmT56MZcuWYeLEidiyZQu8Xi8A4Mknn8S6devkn6+++gqffvopAOCTTz7BeeedhxNPPBGvv/46vvjiC9x6661JNRbvuusufP311zjppJOwYsUKTJ48Ga+88kpOtuzvQGQYBnzMIajkHrKRbxozg1jXbz5+n1zMYWOMKT0S7z3xvtWm/+cAkD/L6/Vi4cKFSX/HL7/8Ehs3boTVak35Hlrfb6FkigLSBK8dYuQ7ua5KosLJbKRlRCtS17OhtLRi0N/2UV5AKymcNEeye6c/LAcE42Oe0o+1JLOKmGyvFWKzwGTbxxVOVnlvSahPSgU3OQ6KQv89PRfl5X/T/kY7Eu0ulX2js1RxoHNs7tDqpyMOsxE3HT0Odxw7AZWxGliVNhPuOHYCbjp6HBxmbQSvDMNg9uzZWLhwIb744guYzWa88sorqK+vR2NjI3744QeMGzcu6ae5WXSofvzxxxg1ahRuvfVWzJw5E+PHj8e2bdsGfMaECRPwy1/+Em+//TZOP/10PPvsswCASZMmyXUUJVauXIkJEybIaspsKL2HTOy///748MMPUzbgcbvdaGxsTHmfkydPQn+fsFQ7y6RQLr9+/XoEAgH5/59++imcTieampowduxYmM3mpM/mOA5r1qzB5MmTlf56mDFjBjZs2DDg7zhu3DiwrLKvvdksbpyj0dLoQC9HvlN2JKUJXkv6b+YpVaE4eFzpo69ke23pH/lOVDjVUw0nzah1iPO8IABdvuT5ZlgF2V1LUjVZoGY6xaH/AZb2NsUhU8M6WmO1pb/tW3uDEATAZGDkdYBQHykoEuEFOaOTUu6LQyoFNzXTyQylfuuM1WTAgqPG4pa549ET5FBhNYHjec26ua5atQrLly/HvHnz4PF4sGrVKnR0dGDSpEkAxNqGV199NSoqKnD88ccjFArhs88+w969e3Httddi/Pjx2L59O1544QUceOCBeOONN5LUkoFAAAsWLMCZZ56J5uZm7Ny5E2vWrMEZZ5wBALjuuutw4IEH4p577sHZZ5+NTz75BI888siAzuKZyHYPSpg/fz6WLVuGc845BzfffDMqKirw6aefYtasWZg4cSIWLFiAO++8E2PHjsW0adPw7LPPYt26dXj++efh7+eQlFJgldb1CIfDuPjii3Hbbbdh69atuPPOOzF//nywLAuHw4ErrrgCCxYsQHV1NUaOHInFixfD7/fj4osvVvz73XHHHTj55JMxcuRInHnmmWBZFuvXr8dXX32Fe++9V9F7jBo1CgzD4PXXX8eJJ54Im80m1zLVg/6bGj5B4UQTvLb0tz2lYhaH/pFvhmHoAFsk0o35epeFFE4aYjSwqLGb0OXn0O4NweOyxOcbUlRqSn8VMRflsVtSOJGiUlP6254cxMUh0e7SGkv7m+LQX0UspX0Pc1nBUp1EzbAYDaiwGtETjKDdG0aV3Sw3MaJyB9rSX/QRikTR5RedxTTfpIZ22yWAw2yE2ciizmmB2chqpqQERLXgBx98gBNPPBETJkzAbbfdhiVLluCEE8TampdccgmeeuopPPvss5gyZQqOOOIIPPfcc7Ki8tRTT8Uvf/lLzJ8/H9OmTcPHH3+M22+/XX5/g8GArq4unH/++ZgwYQLOOussnHDCCVi4cCEAUen34osv4oUXXsB+++2HO+64A3fffXdSx+9sZLsHJdTU1GDFihXwer044ogjcMABB+DJJ5+UU6GvvvpqXHvttbjuuuswZcoUvPnmm3jttdcwfvx4GPspEiVFpdGgbGGdO3cuxo8fj8MPPxxnn302Tj31VNx1113y9fvuuw9nnHEGfvazn2HGjBnYtGkT3nrrLVRVVSn+/Y477ji8/vrrePvtt3HggQfi4IMPxoMPPohRo0Ypfo/hw4fLTXnq6+sxf/58xa/Vgv6OA0nhxDBQvekUkUy61G/a1GhLXUxVwEUF9ATFRkbkqCwO/RsZUcp98ei/mW+hMV8U+tu9rS8EQQCMLCPPRYQ2yLbv65e1QA5iTZHsHuB4+MJi9hCtscWhv4JbUpaR3bVngII7pqik/Y22DAiAxxzEViOLKtvAnhkEKSrLjkmTJuHNN9/M+Jyf/vSn+OlPf5r2+uLFi7F48eKkx6655hoAYrrwX//614zvf8YZZ8gKy1Rs3bp1wGPrYk1slNwDINbJTHT+pWL//ffHW2+9lfIay7K48847ceeddw64ZjIwaGwaiU5vCDUOM77d3QcAOOrIo+R6HxIXXHBBSifswoULZedtf6xWK5YuXYqlS5emvH7kkUcO+Jxp06YNeOy4447Dcccdl/I9AAx4PgB0d3cn/f/222/P2QmsFdJBKR59jSmcnKRw0pr+abBUeLs4WE0GuCxG9IUiaPeG4DQbZIUT2V5b+jcWoQYLxcPjtODbdi+py4pMXX9Vn5Ry77aQwkljah2kqNQDh9kAm4lFgBPrI5oMDDpjJSdojdWWAfNNL+1tikWd04yNnb4BDjPa32jLANGHlHJfYVXca6TcoNM9QeSIMbZh52JNZ6TUb5NCRSWRH1Ikys9F4QtFKOpdRPpHvknhVDwSNzaSwsnAMvLfhNCGgRtKcs4Xi3QKbko/1hZpTukJRhCKRBOaipDdtSa+xvZXEZPttURsFhgPxLbGnGUWI4tqOymctCRt7XPaV2rOANvL5VXI9loi2b3LzyES5WUHMdk9PeSoJIgckdR7kagAQRDizXRI1acpTosB1liH6XZvmByVRaTOkSb1mxZXzUncUMpNRVykcNKadIcomm+0pzbB9qFIFB2Swoma6WhKpdUkB2I7EuYbUvVpDzXT0Y/E/U3i3oYUTtoyoC4rNRUpGolqVn84IjfVoflGW2ocZrkhb5efS1JUEqmh1G+CyBGpu3eEjzspGUZZ1+/nnntOy1sb0kiR7+3dAXT4QnJq1DDa1GhO/3p9VGy+eCR2CZRmGLK79sQj32FEeYG6rRcRecz7QmhLUDjV2KlOopawLIM6pxmtvSF0+MLx9GNS9WlOfMxTeZVik6hmDUXEDClaY7Un0e6CIMip32R77Um0vWR3u8kAt5XcQlpiYBnU2M3o9IXR7g3FU+5pnk8LScAIIkfk1O+oAE5K+2ZZir4WAU9Cl8Bd5CwrGh5XXOEUjvCyyoxsrz11KRSVZHftqbGbwDCAIABdPrJ9MUk8RCWm3NMaqz2Jyr4WaiBVNBLtHuCi2EsKp6KRaHua54uHpOqL8AK6Axw10ykiieWcEu1Oa6z2JCqJSfSRHXJUEkSOSCneEZ5HmKf6lMUk1SGKIlHak2T32MJqNpDCqRiksj2piLXHmDC++6cEEtqSuJGn9OPikuoQRSn32pPKWWYzsagghZPm1KWYb2iN1R6L0SCP7/Z+QSlCW5JEH7S3KSrJ5Zyo3EE2aAUkiBwxpkj9pvqUxSGV04YiUdoj1XCK8AK+iXW5b3RbKPpaBBLT7vuCEQA05ouFxymm6GzbGyCFUxFJ3Mi3UEOXoiLbvi9BRewm22uNNM8HOB7fd3gBiGOe1ljt6d/ICCAVcbHwOC3oCUawucsHbygKgNbYYpB8lqKU+2JCts+NQeNd2bNnD8477zy43W5UVlbi4osvhtfrzfj8X/ziF5g4cSJsNhtGjhyJq6++Gj09PUW8a2IoIqknuaggd/w2k6KyKNSliALShlJ7rAm1a77YJc6htLAWhySlDTnni4pk+3Ut4pi3mVhU2qgTrNakUpdRsfniUEdpsLrgsBhhNxkAJKyxtLcpCh7XQEUljfniIDnopTHvthrhtJCGSmuS11hq6FJMpHPs7r4QlTtQwKBxVJ533nn4+uuv8c477+D111/HBx98gMsuuyzt81taWtDS0oIHHngAX331FZ577jm8+eabuPjii4t418RQxMiKXxteEBDipNTvQfNVGtRIi+uOvQHs8YsKJ1pci4Nk+/UtvQAoVaFYULkD/Ug15knhpD3S4bU3GMGWLj8AGvPFQrL9D11+9IVEBTfZvjhItpfnG9rbFIVUCm4a88WB9pX6IM01XX4OO7op/biYSGN+U6cPgZgPgUpNpGdQhC2+/fZbvPnmm1izZg1mzpwJAFi2bBlOPPFEPPDAA2hsbBzwmv322w8vv/yy/P+xY8di0aJF+J//+R9EIhEYjYPiVydKEJYRf3gB8HNiqgI5KouDFPmWFE5WI4sqUjgVBY/TjE2dPjnyTYeo4pBYS0hScFP0tTjU9VN7kN2LQ6XNBCPLIMIL8lxPti8O/VXELosRLqqTWBQ8Tgu27g3E11g6vBYFaczv9oaovEqRkRTctMYWl2q7WT7HSk5isn1x6K8irrabYIup6YmBDArvyieffILKykrZSQkAxxxzDFiWxapVqxS/T09PD9xud0YnZSgUQm9vb9IPkZ0LLrgAP/7xjzM+58gjj8Q111yj6ufeddddmDZtmqrvmQ2GYWCMOSZDEUr9LibShnJzTGVDXeqKR3/b0yGqOEh27/SF0RskhVMxGTjfUK2+YsAwTIr5hhq6FAOyu36k2t8Q2uNJSMWUxAcUiC0Oku1pX1lcDCyDWkey7Wm+KQ50lsqNQeGobGtrg8fjSXrMaDSiuroabW1tit6js7MT99xzT8Z0cQD49a9/jYqKCvmnqakp7/tWCs/5IETDiPrbIUTD4Dmf5p+pNg8//DCee+45vW+jaJjYZOfYYFZUvv/++2AYBt3d3XrfSlakCV6CJvjiIanLJGhTUxxqHGYk+uKdFgPcVlIRFwNPvzE/jJw2RaO/7clJXBzI7vpR50q2Pe1vikOdI3ler7KRwqlYDNjT076yaNB5Sh/6253OUpnR1bty0003gWGYjD/fffddwZ/T29uLk046CZMnT8Zdd92V8bk333wzenp65J8dO3YU/PmZ4CNB9Hz2ALY/MUL+6flsCfhIUNPPTSQcDmd/UhYqKipQWVlZ+M0MEoz9HJWkqCwOAw9RNMEXC1pc9cHAMqi1x8c9NVgoHjTm9WPgIYqcxMWAxrx+kO31wWxMbpJGdi8eA8Y87W+KRn/bUyC2OPQ/x5JzPjO6Oiqvu+46fPvttxl/xowZg4aGBrS3tye9NhKJYM+ePWhoaMj4GX19fTj++OPhcrnwyiuvwGTKrESxWCxwu91JP0oRBAE851P8Ew33oWfNb9C9ahH4UDcAgA91o3vVvehZsxjRcK/i9xIEQfF9HnnkkZg/fz6uueYa1NbW4rjjjgMAfPXVVzjhhBPgdDpRX1+Pn/3sZ+js7JRf99JLL2HKlCmw2WyoqanBMcccA59PVH/2T/32+Xw4//zz4XQ6MWzYMCxZsmTAfTAMg3/84x9Jj1VWViYpM2+88UZMmDABdrsdY8aMwe233w6O4xT/rgDw9ddf4+STT4bb7YbL5cJhhx2GzZs3AwB4nsfdd9+NESNGwGKxYNq0aXjzzTfl127duhUMw+DFF1/EYYcdBpvNhgMPPBA7tmzG1+s/x/knHYXD9xmBk086CR0dHfLrJHssXLgQdXV1cLvduPzyy5OcwqFQCFdffTU8Hg+sVivmzJmDNWvWyNclpePy5csxc+ZM2O12HHroodiwYUPS7/fqq69ixowZsFqtGDNmDBYuXIhIJJJk56eeegqnnXYa7HY7xo8fj9dee03+/Y466igAQFVVFRiGwQUXXJCTfYtJ/8g3FSAuHhR91Y9E25PdiweNef1I3MxX2kywm6lOYjGoc/RXEdOYLxYDDrBk+6KRaHuye/Egp41+JNq+1mGGxUgq4mJA+8rc0HXnV1dXh7q6uqzPO+SQQ9Dd3Y21a9figAMOAACsWLECPM/joIMOSvu63t5eHHfccbBYLHjttddgtWo7GISIH9serVL0XNZWi6aLNqJ33aMpr/euewQVM6/DjmfGgw90pnxOIqOu2gvG5FB8r3/4wx9wxRVXYOXKlQCA7u5uHH300bjkkkvw4IMPIhAI4MYbb8RZZ52FFStWoLW1Feeeey4WL16M0047DX19ffjwww/TOkgXLFiA//znP3j11Vfh8Xhwyy234PPPP8+5nqTL5cJzzz2HxsZGfPnll7j00kvhcrlwww03KHr9rl27cPjhh+PII4/EihUr4Ha7sXLlStmR9/DDD2PJkiX4/e9/j+nTp+OZZ57Bqaeeiq+//hrjx4+X3+fOO+/EQw89hJEjR+Kiiy7CNf/vQphtDlx316/hdjpwy1UX4Y477sDjjz8uv2b58uWwWq14//33sXXrVlx44YWoqanBokWLAAA33HADXn75ZfzhD3/AqFGjsHjxYhx33HHYtGkTqqur5fe59dZbsWTJEtTV1eHyyy/HRRddJP/dPvzwQ5x//vlYunSp7ICVyhvceeed8nssXLgQixcvxv33349ly5bhvPPOw7Zt29DU1ISXX34ZZ5xxBjZs2AC32w2brXRTvaTId3dAdFZT5Lt40CFKPzxOM77eLf6bxnzxIAW3ftQlbOZJZVM8HBYjHGYDfGGxVh+N+eIxMA2WFE7FwuO04PsOUXhBzrLiQYpK/UhaY2nMFw231QizgY03x6Qxn5FBEaKeNGkSjj/+eFx66aX43e9+B47jMH/+fJxzzjlyx+9du3Zh7ty5+OMf/4hZs2aht7cX8+bNg9/vx5///Oekxjh1dXUwGPSNHBjsDYj622UlZX/4UDeigQ4Y7A2KHJW5Mn78eCxevFj+/7333ovp06fjV7/6lfzYM888g6amJnz//ffwer2IRCI4/fTTMWrUKADAlClTUr631+vF008/jT//+c+YO3cuANExOmLEiJzv87bbbpP/PXr0aFx//fV44YUXFDsqH330UVRUVOCFF16Q1bQTJkyQrz/wwAO48cYbcc455wAAfvOb3+C9997DQw89hEcfjTuRr7/+ell5+r//+78499xz8dhfX8XUAw+Gy2LExRdfPKBGp9lsxjPPPAO73Y59990Xd999NxYsWIB77rkHgUAAjz/+OJ577jmccMIJAIAnn3wS77zzDp5++mksWLBAfp9FixbhiCOOACCWSzjppJMQDAZhtVqxcOFC3HTTTfj5z38OABgzZgzuuece3HDDDUmOygsuuADnnnsuAOBXv/oVli5ditWrV+P444+XnaIej2dQpO97nGZyVOpA4oaywmqEwzIolo8hgccVtz0pnIrHwENU6QZxhhpJKmJy2BQVj9OCLXuomU6xSRzzpHAqLqSo1AdP/7qstKcvGoljnpxlxUNsFmjGzh6xxB+N+cwMmpPm888/j/nz52Pu3LlgWRZnnHEGli5dKl/nOA4bNmyA3y9urj7//HO5I/i4ceOS3mvLli0YPXq06vfIGO0YddVe5c9nTWAtlSmdlaylEkZHIxrP+VDxZ+eCpEyVWL9+Pd577z04nc4Bz928eTPmzZuHuXPnYsqUKTjuuOMwb948nHnmmaiqGqgg3bx5M8LhcJLatbq6GhMnTszpHgHgb3/7G5YuXYrNmzfLztJc0vHXrVuHww47LGXKf29vL1paWjB79uykx2fPno3169cnPbb//vvL/66vrwcATJy8L2wmA6wmFvX19QPKE0ydOhV2e/zvcsghh8Dr9WLHjh3o6ekBx3FJn20ymTBr1ix8++23aT972LBhAID29naMHDkS69evx8qVK2WVJgBEo1EEg0H4/X758xPfw+FwwO12D7jfwYLHacEeP4cGlwWjKslxUCw8TgtqHWY0uCyosA6apWNIUJdg+3G1uc31RP64rUY0uq2otpvQ1heiGk5FxOM0y2N+n7qB+xJCOzxOM/pCEXGNraL5plgkjvkauzn7CwjVSFxjx9bQmC8W1TYzPE4zPE4LdntDaHDRGlssEvf04+uUZ2QSheNxWhCM8LE1ls6xmRg0p83q6mr85S9/SXt99OjRSWnIRx55ZE51G9WAYZic0q95zgf3tPnoXnXvgGvuafMh8BzYHN4vFxyO5Pf1er045ZRT8Jvf/GbAc4cNGwaDwYB33nkHH3/8Md5++20sW7YMt956K1atWoXm5ua87oFhmAF/o8T6k5988gnOO+88LFy4EMcdd5ysjExV7zIdaqUxJzo6mVgL3qkjquF022FiWQiCWO9SC1J9tvRZXq8XCxcuxOmnnz7gdYmlDvo7ahmG0ex+teY3J03C/o1utHvDaHRb4QtH4KD6ZZrTWGHBllvnot0bRr3TQnYvIpcdNBK/PnEftHvDGOYi2xcLPxfFhpuOQrs3DI/TjGiR9xTlzNHja+X5poHGfFFZcsq+mDqc1thiM7LSRmusTlx56Gg8cMpkWmOLTCASxeZb5sprbDjKw2TQtX1G2XDshDr8dMZwWmN14KEf74vpwytojVUAWUVHWJMDFQeKKcy96x4BH+oGa6mEe9p8VBx4A1hj8eTAM2bMwMsvv4zRo0fDaEw9LBiGwezZszF79mzccccdGDVqFF555RVce+21Sc8bO3YsTCYTVq1ahZEjRwIA9u7di++//15OYQbEFPzW1lb5/xs3bpQVsQDw8ccfY9SoUbj11lvlx7Zt25bT77X//vvjD3/4AziOG+Csc7vdaGxsxMqVK5Pua+XKlZg1a1ba9+R58bD6dVsv7D4GBpZBTzAy4Hnr169HIBCQnaWffvopnE4nmpqaUFtbC7PZjJUrV8qp9BzHYc2aNbjmmmsU/34zZszAhg0bBqiGc8FsFiP30Wg07/coFkEuin9/146Tnl6N7gCHSpsJV89pxk1Hj4PVRGlSWhHkonjogy1Y9tEWsnuRCXJR/P2/LVj20VayfREJclEsfm8zjXkdCHJRPLN6O415HQhyUby5oR0nP0NrbDEJclE89CGtsXoQ5KJ4idbYokNrrH4EuSieXUNrrB4EuSje3tCBU59ZQ7ZXADkqdYY1WlEx8zpUzroJfKgHrKVCVFIW0UkJAFdddRWefPJJnHvuubjhhhtQXV2NTZs24YUXXsBTTz2Fzz77DMuXL8e8efPg8XiwatUqdHR0YNKkSQPey+l04uKLL8aCBQtQU1MDj8eDW2+9FSybHCU7+uij8cgjj+CQQw5BNBrFjTfemORMHD9+PLZv344XXngBBx54IN544w288sorOf1e8+fPx7Jly3DOOefg5ptvRkVFBT799FPMmjULEydOxIIFC3DnnXdi7NixmDZtGp599lmsW7cOzz//fMr3i/ICuvxc7N/xx7qDHARB/LeBFVWP4XAYF198MW677TZs3boVd955J+bPnw+WZeFwOHDFFVdgwYIFqK6uxsiRI7F48WL4/X5cfPHFin+/O+64AyeffDJGjhyJM888EyzLYv369fjqq69w770DlbqpGDVqFBiGweuvv44TTzwRNpstZQkAvfGFI1j83mbc++5G+bHuAIe73/keALDgqLEUkdIAye73xOwMkN2LRdz2NOaLCY15/aAxrx+0xuoDzTf6QfONPtCY1w8a8/pBa2zukL66BGBNDjAGMwz2OjAGs2bp3pmQlIXRaBTz5s3DlClTcM0116CyshIsy8LtduODDz7AiSeeiAkTJuC2227DkiVL5EYw/bn//vtx2GGH4ZRTTsExxxyDOXPmDKiLuWTJEjQ1NeGwww7DT3/6U1x//fVJNR1PPfVU/PKXv8T8+fMxbdo0fPzxx7j99ttz+r1qamqwYsUKeL1eHHHEETjggAPw5JNPyg7Rq6++Gtdeey2uu+46TJkyBW+++SZee+21pI7fiTAM0B0Ip7zGQ0AsMxsAMHfuXIwfPx6HH344zj77bJx66qm466675Ov33XcfzjjjDPzsZz/DjBkzsGnTJrz11lsp636m47jjjsPrr7+Ot99+GwceeCAOPvhgPPjgg7JKUwnDhw+Xm/LU19dj/vz5il9bTEwsi2UfbUl5belHW2BiaTrTArK7fpDt9YHsrh9ke/0g2+sD2V0/yPb6QHbXD7K9fpDtc4cRil3IcZDR29uLiooK9PT0DGjiEgwGsWXLFjQ3NyfVAySGLlyUx/qW3rTXpza6YTKwuOCCC9Dd3Y1//OMfxbs5nSjW96DdG0LDXW+nvb77rnmoc1IhbrUhu+sH2V4fyO76QbbXD7K9PpDd9YNsrw9kd/0g2+sH2T5OJv9aIuS6JYgcMLCMnNqdyzWicCqtJlTaBnZvB4BKmwkV1tTXiMIgu+sH2V4fyO76QbbXD7K9PpDd9YNsrw9kd/0g2+sH2T53yFFJEDkgCEB9mmhHvdMC0idrB8fzuHpO6g7zV89pBjdIu5iXOmR3/SDb6wPZXT/I9vpBttcHsrt+kO31geyuH2R7/SDb5w5V7CSIHDCwDBpcoqNytzckN8+pd1rQ4LKAjSkqn3vuOR3vcmjiMBtx09Fid/Ol1CWwaJDd9YNsrw9kd/0g2+sH2V4fyO76QbbXB7K7fpDt9YNsnztUozILVKOSSEWUFxvnSI5KQUDZpn0X+3vgC0dgYln0BDlUWE3geJ66pBUBsrt+kO31geyuH2R7/SDb6wPZXT/I9vpAdtcPsr1+kO2V16gsL6toBE9S3bJDckqyhphzsjx9lACKP/6lyVwqOGymChZFgeyuH2R7fSC76wfZXj/I9vpAdtcPsr0+kN31g2yvH2R75ZCjsgDMZjNYlkVLSwvq6upgNpvBMGXssSLKCkEQEA6H0dHRAZZlYTab9b4lgiAIgiAIgiAIgiAGMeSoLACWZdHc3IzW1la0tLTofTsEoQt2ux0jR44Ey1JEiCAIgiAIgiAIgiCI/CFHZYGYzWaMHDkSkUgE0WhU79shiKJiMBhgNBpJSUwQBEEQBEEQBEEQRMGQo1IFGIaByWSCyWTS+1YIgiAIgiAIgiAIgiAIYlBCuZoEQRAEQRAEQRAEQRAEQegOOSoJgiAIgiAIgiAIgiAIgtAdclQSBEEQBEEQBEEQBEEQBKE7VKMyC4IgAAB6e3t1vhOCIAiCIAiCIAiCIAiCGHxIfjXJz5YOclRmoa+vDwDQ1NSk850QBEEQBEEQBEEQBEEQxOClr68PFRUVaa8zQjZXZpnD8zxaWlrgcrnAMIzet6M6vb29aGpqwo4dO+B2u/W+HaJMoHFH6AGNO6LY0Jgj9IDGHaEHNO6IYkNjjtADGneFIQgC+vr60NjYCJZNX4mSFJVZYFkWI0aM0Ps2NMftdtMXjSg6NO4IPaBxRxQbGnOEHtC4I/SAxh1RbGjMEXpA4y5/MikpJaiZDkEQBEEQBEEQBEEQBEEQukOOSoIgCIIgCIIgCIIgCIIgdIcclWWOxWLBnXfeCYvFovetEGUEjTtCD2jcEcWGxhyhBzTuCD2gcUcUGxpzhB7QuCsO1EyHIAiCIAiCIAiCIAiCIAjdIUUlQRAEQRAEQRAEQRAEQRC6Q45KgiAIgiAIgiAIgiAIgiB0hxyVBEEQBEEQBEEQBEEQBEHoDjkqCYIgCIIgCIIgCIIgCILQHXJUEgRBEARBEARBEARBEAShO+SoLFE++OADnHLKKWhsbATDMPjHP/6RdH337t244IIL0NjYCLvdjuOPPx4bN25M+V6CIOCEE05I+T7Lly/HoYceCpfLhYaGBtx4442IRCJZ7+/999/HjBkzYLFYMG7cODz33HM53T9Rmqgx7o488kgwDJP0c/nllyc9Z/v27TjppJNgt9vh8XiwYMECRePu73//O/bZZx9YrVZMmTIF//rXv3K+P6K0KMaYW79+Pc4991w0NTXBZrNh0qRJePjhhxXdX7Yxd8EFFwz47OOPPz4/YxBFo1hznURXVxdGjBgBhmHQ3d2d9f6yjbv/+7//w7x581BTUwOGYbBu3bpcfn1CJ4o17vpfZxgGL7zwQtb7ozV26FGMMffcc8+lHHMMw6C9vT3j/f3/9u49psr6jwP4GwS5aEdk3DVoeMtbKroYVjKSAcoWLFsEujmHaElbWmmeidJw1dQWdtHaNCmkEXQxGSpGgqZxpugOCuhULsakg2yIgJCQx8/vj36cPMq5AIdzuLxf2/mD5/me5/t5nr35fnm+nAvn2OHJWmMd72PpYZZaP1GpVHjxxRcxZswYKBQKLFq0CH///bdu/+3bt7F8+XIoFAq4ubkhMTERd+/eNVmfqdy1tbVh/fr1CAgIgIuLCxYuXIjS0tI+XYvhgAuVg1R7ezvmzJmDPXv2PLZPRBAbG4uamhocPnwYarUaAQEBCA8PR3t7+2Ptd+/eDTs7u8e2X7x4EUuXLkVUVBTUajVycnKQl5eHzZs3G62ttrYW0dHRCAsLQ1lZGdavX4/Vq1fj+PHjZtVPg5elcpeUlASNRqN77Ny5U7dPq9UiOjoaXV1dKCkpwbfffotvvvkG27ZtM1pbSUkJ4uPjkZiYCLVajdjYWMTGxqKioqLX9dHgYY3MXbhwAV5eXsjKykJlZSW2bNkCpVKJL774wmhtpjLXLSoqSq/v7OzsflwRsgZr5O5hiYmJeOaZZ8yqzZzctbe34/nnn8eOHTt6cdZka9bMXUZGhl6b2NhYo7Vxjh2erJG5uLg4vX0ajQaRkZEIDQ2Fl5eXwdo4xw5f1sgd72PpUZbInUqlQlRUFCIiInDu3DmUlpbizTffhL39f8tmy5cvR2VlJQoLC5Gfn4/ff/8da9asMVqbOblbvXo1CgsLcfDgQZSXlyMiIgLh4eGor6+3wNUZgoQGPQBy6NAh3c9Xr14VAFJRUaHbptVqxdPTU/bt26f3XLVaLRMmTBCNRvPYcZRKpSxYsECvfV5enjg7O0tra6vBejZt2iQzZ87U2xYXFyeRkZFm1U9DQ19zFxoaKm+99ZbB4x49elTs7e2loaFBt+3LL78UhUIhnZ2dBp/36quvSnR0tN624OBgWbt2ba/qo8FroDLXk3Xr1klYWJjRNqYyJyKycuVKiYmJ6VXfNLgMdO727t0roaGhcuLECQEgzc3NRtubk7tutbW1AkDUarXJOmhwGcjc9eXvLs6xw5+15tjGxkZxdHSUzMxMo+04x44MA5U73seSMX3NXXBwsKSkpBg87uXLlwWAlJaW6rYdO3ZM7OzspL6+3uDzTOWuo6NDRo0aJfn5+XptgoKCZMuWLcZPdpjiKyqHoM7OTgCAs7Ozbpu9vT2cnJxw5swZ3baOjg4kJCRgz5498PHx6fE4Dx8DAFxcXHDv3j1cuHDBYP8qlQrh4eF62yIjI6FSqfp0PjQ0mJs7APjuu+/g4eGBWbNmQalUoqOjQ7dPpVJh9uzZ8Pb21m2LjIxEa2srKisrDfZvKne9qY+GBktlrictLS1wd3c32sbcse7kyZPw8vLCtGnT8MYbb6CpqcnkudHgZcncXb58GWlpacjMzNT7b7wxnGNHJkuPd8nJyfDw8MCzzz6LAwcOQESM9s85duQZqDk2MzMTrq6ueOWVV4z2zzl2ZLJU7ngfS71hTu4aGxtx9uxZeHl5YeHChfD29kZoaKheLlUqFdzc3LBgwQLdtvDwcNjb2+Ps2bMG+zeVu/v370Or1faY6ZE6x3Khcgh6+umn4e/vD6VSiebmZnR1dWHHjh24efMmNBqNrt2GDRuwcOFCxMTE9HicyMhIlJSUIDs7G1qtFvX19UhLSwMAveM8qqGhQW+RCQC8vb3R2tqq9/kNNLyYm7uEhARkZWWhuLgYSqUSBw8exIoVK3T7DeWne58hhp7X/Rxz66Ohw1KZe1RJSQlycnJMvk3DVOaAf9+SlpmZiRMnTmDHjh04deoUlixZAq1W28ezJluzVO46OzsRHx+PXbt2wd/f3+z+zckdDT+WHO/S0tKQm5uLwsJCLFu2DOvWrcPnn39utH/OsSPPQM2xX3/9NRISEuDi4mK0f86xI5Olcsf7WOoNc3JXU1MDAHj//feRlJSEgoICBAUFYfHixbrPsmxoaHjsIy0cHBzg7u7ep/vY7tw98cQTCAkJwfbt2/HXX39Bq9UiKysLKpVqxM6xXKgcghwdHfHzzz/j2rVrcHd3h6urK4qLi7FkyRLdKzby8vJQVFSE3bt3GzxOREQEdu3ahddffx1OTk6YOnUqli5dCgC644wdO1b3MPQlATQymJM7AFizZg0iIyMxe/ZsLF++HJmZmTh06BCqq6vN6qeurk4vdx9++KFF66OhYyAyV1FRgZiYGKSmpiIiIgJA3zMHAK+99hpeeuklzJ49G7GxscjPz0dpaSlOnjzZ7/Mn27BU7pRKJaZPn27whr4/uaPhx5Lj3datW/Hcc89h3rx5eO+997Bp0ybs2rULAOdY+s9AzLEqlQpXrlxBYmKibhvnWHqYpXLH+1jqDXNy9+DBAwDA2rVrsWrVKsybNw/p6emYNm0aDhw4YHZffc3dwYMHISKYMGECnJyc8NlnnyE+Pn7EzrEOti6A+mb+/PkoKytDS0sLurq64OnpieDgYN3LkIuKilBdXQ03Nze95y1btgwvvPCCboJ/++23sWHDBmg0GowfPx43btyAUqlEYGAgAOh9k6hCoQAA+Pj44NatW3rHvXXrFhQKhcn/ntLQZip3PQkODgYAVFVVYdKkSfDx8cG5c+f02nTnycfHB35+fnq56357rqHcPfyxBn2pjwY3S2Su2+XLl7F48WKsWbMGKSkpuu39ydyjAgMD4eHhgaqqKixevLhX50qDhyVyV1RUhPLycvz4448AoHvrrYeHB7Zs2YKtW7daLHc0PFhyvHu0zfbt29HZ2ck5lvRYOnP79+/H3LlzMX/+fN02zrH0KEvljvex1Bumcufr6wsAmDFjht7zpk+fjrq6OgD/5qexsVFv//3793H79m3d2NXX3E2aNAmnTp1Ce3s7Wltb4evri7i4OF2eR5qRuTw7jIwbNw6enp64fv06zp8/r3ub9+bNm3Hp0iWUlZXpHgCQnp6OjIwMvWPY2dnBz88PLi4uyM7OxpNPPomgoCAAwOTJk3WP7pc5h4SE4MSJE3rHKCwsREhIyACfLQ0WhnLXk+7sdQ/+ISEhKC8v1xvkCwsLoVAoMGPGDDg4OOjlrvsP2t7krjf10dDQn8wBQGVlJcLCwrBy5Up88MEHeu0tkbluN2/eRFNTk17fNHT1J3c//fQTLl68qJuD9+/fDwA4ffo0kpOTLZo7Gl76O9711Gb8+PFwcnLiHEs9skTm7t69i9zcXL1XUwKcY8kwS+SO97HUW4Zy99RTT8HPzw9Xr17Va3/t2jUEBAQA+Dc/d+7c0fsc1KKiIjx48EC3mN7f3I0ZMwa+vr5obm7G8ePHR+4ca+Mv8yED2traRK1Wi1qtFgDyySefiFqtlj///FNERHJzc6W4uFiqq6vll19+kYCAAHn55ZeNHhM9fGvZzp075dKlS1JRUSFpaWni6Oho8pvNampqxNXVVTZu3ChXrlyRPXv2yKhRo6SgoMDs+mlw6m/uqqqqJC0tTc6fPy+1tbVy+PBhCQwMlEWLFuna3L9/X2bNmiURERFSVlYmBQUF4unpKUql0mhtf/zxhzg4OMjHH38sV65ckdTUVHF0dJTy8nJdm778XpBtWSNz5eXl4unpKStWrBCNRqN7NDY2Gq3NVOba2trk3XffFZVKJbW1tfLbb79JUFCQTJkyRe7duzcAV4ssxRq5e1RxcbFZ3/ptzljX1NQkarVajhw5IgDk+++/F7VaLRqNpn8XhgaUNXKXl5cn+/btk/Lycrl+/brs3btXXF1dZdu2bUZr4xw7PFlzrNu/f784OzubHOO6cY4dvqyVO97H0sMssX6Snp4uCoVCfvjhB7l+/bqkpKSIs7OzVFVV6dpERUXJvHnz5OzZs3LmzBmZMmWKxMfHG63NnNwVFBTIsWPHpKamRn799VeZM2eOBAcHS1dXlwWv0tDBhcpBqvuG5tHHypUrRUTk008/lYkTJ4qjo6P4+/tLSkqKdHZ2Gj1mTwuVYWFhMm7cOHF2dpbg4GA5evSo2fXNnTtXRo8eLYGBgZKRkdGr+mlw6m/u6urqZNGiReLu7i5OTk4yefJk2bhxo7S0tOj1c+PGDVmyZIm4uLiIh4eHvPPOO/LPP/+YrC83N1emTp0qo0ePlpkzZ8qRI0f09vfl94JsyxqZS01N7bGPgIAAk/UZy1xHR4dERESIp6enODo6SkBAgCQlJUlDQ4PFrg8NDGuNdT31ac5NvKmxLiMjo8f6U1NT+3I5yEqskbtjx47J3LlzZezYsTJmzBiZM2eOfPXVV6LVak3Wxzl2+LHmWBcSEiIJCQm9qo9z7PBkrdzxPpYeZqn1k48++kgmTpworq6uEhISIqdPn9bb39TUJPHx8TJ27FhRKBSyatUqaWtrM6s+Y7nLycmRwMBAGT16tPj4+EhycrLcuXOnz9djqLMT+f+HJhERERERERERERHZCD+jkoiIiIiIiIiIiGyOC5VERERERERERERkc1yoJCIiIiIiIiIiIpvjQiURERERERERERHZHBcqiYiIiIiIiIiIyOa4UElEREREREREREQ2x4VKIiIiIiIiIiIisjkuVBIREREREREREZHNcaGSiIiIiIiIiIiIbI4LlURERERERERERGRzXKgkIiIiIiIiIiIim/sfQFpOxmtlgWkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series(seasonal, resid, labels=[\"seasonal component\", \"residual component\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ok, now the forecast!\n",
    "\n",
    "... of what ??\n",
    "\n",
    "ah yes, residual plus trend, because seasonal just repeats itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>, <AxesSubplot: >)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# forecast this:\n",
    "plot_series(trend + resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1949-01   NaN\n",
       "1949-02   NaN\n",
       "1949-03   NaN\n",
       "1949-04   NaN\n",
       "1949-05   NaN\n",
       "           ..\n",
       "1960-08   NaN\n",
       "1960-09   NaN\n",
       "1960-10   NaN\n",
       "1960-11   NaN\n",
       "1960-12   NaN\n",
       "Freq: M, Name: trend, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this has nans??\n",
    "trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1949-01    4.804314\n",
       "1949-02    4.885097\n",
       "1949-03    4.864689\n",
       "1949-04    4.872858\n",
       "1949-05    4.804757\n",
       "             ...   \n",
       "1960-08    6.202368\n",
       "1960-09    6.165645\n",
       "1960-10    6.208669\n",
       "1960-11    6.181992\n",
       "1960-12    6.168741\n",
       "Freq: M, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ok, forecast this instead then:\n",
    "y_to_forecast = logy - seasonal\n",
    "\n",
    "# phew, no nans!\n",
    "y_to_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>, <AxesSubplot: >)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sktime.forecasting.trend import PolynomialTrendForecaster\n",
    "\n",
    "f = PolynomialTrendForecaster(degree=2)\n",
    "f.fit(y_to_forecast, fh=list(range(1, 13)))\n",
    "y_fcst = f.predict()\n",
    "\n",
    "plot_series(y_to_forecast, y_fcst)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looks reasonable!\n",
    "\n",
    "Now to turn this into a forecast of the original y ...\n",
    "\n",
    "* add seasonal\n",
    "* invert the logarithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1961-01    6.195931\n",
       "1961-02    6.202857\n",
       "1961-03    6.209740\n",
       "1961-04    6.216580\n",
       "1961-05    6.223378\n",
       "1961-06    6.230132\n",
       "1961-07    6.236843\n",
       "1961-08    6.243512\n",
       "1961-09    6.250137\n",
       "1961-10    6.256719\n",
       "1961-11    6.263259\n",
       "1961-12    6.269755\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_fcst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1949-01   NaN\n",
       "1949-02   NaN\n",
       "1949-03   NaN\n",
       "1949-04   NaN\n",
       "1949-05   NaN\n",
       "1949-06   NaN\n",
       "1949-07   NaN\n",
       "1949-08   NaN\n",
       "1949-09   NaN\n",
       "1949-10   NaN\n",
       "1949-11   NaN\n",
       "1949-12   NaN\n",
       "1961-01   NaN\n",
       "1961-02   NaN\n",
       "1961-03   NaN\n",
       "1961-04   NaN\n",
       "1961-05   NaN\n",
       "1961-06   NaN\n",
       "1961-07   NaN\n",
       "1961-08   NaN\n",
       "1961-09   NaN\n",
       "1961-10   NaN\n",
       "1961-11   NaN\n",
       "1961-12   NaN\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_fcst_orig = y_fcst + seasonal[0:12]\n",
    "y_fcst_orig_orig = np.exp(y_fcst_orig)\n",
    "\n",
    "y_fcst_orig_orig"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ok, that did not work. Something something pandas indices??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>,\n",
       " <AxesSubplot: ylabel='Number of airline passengers'>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_fcst_orig = y_fcst + seasonal[0:12].values\n",
    "y_fcst_orig_orig = np.exp(y_fcst_orig)\n",
    "\n",
    "plot_series(y, y_fcst_orig_orig)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ok, done! and it only took us 10 years.\n",
    "\n",
    "Maybe there is a better way?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slightly less naive approach - use `sktime` transformers (badly)\n",
    "\n",
    "Ok, surely there is a way where I don't have to fiddle with wildly varying interfaces of every step.\n",
    "\n",
    "Solution: use transformers!\n",
    "\n",
    "Same interface at every step!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.trend import PolynomialTrendForecaster\n",
    "from sktime.transformations.series.boxcox import LogTransformer\n",
    "from sktime.transformations.series.detrend import Deseasonalizer\n",
    "\n",
    "y = load_airline()\n",
    "\n",
    "t_log = LogTransformer()\n",
    "ylog = t_log.fit_transform(y)\n",
    "\n",
    "t_deseason = Deseasonalizer(sp=12)\n",
    "y_deseason = t_deseason.fit_transform(ylog)\n",
    "\n",
    "f = PolynomialTrendForecaster(degree=2)\n",
    "f.fit(y_deseason, fh=list(range(1, 13)))\n",
    "y_fcst = f.predict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hm, but now we need to invert the transformations...\n",
    "\n",
    "fortunately transformers have an inverse transform, standard interface point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>,\n",
       " <AxesSubplot: ylabel='Number of airline passengers'>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_fcst_orig = t_deseason.inverse_transform(y_fcst)\n",
    "# the deseasonalizer remembered the seasonality component! nice!\n",
    "\n",
    "y_fcst_orig_orig = t_log.inverse_transform(y_fcst_orig)\n",
    "\n",
    "plot_series(y, y_fcst_orig_orig)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expert approach - use `sktime` transformers with pipelines!\n",
    "\n",
    "Bragging rights included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>,\n",
       " <AxesSubplot: ylabel='Number of airline passengers'>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sktime.forecasting.trend import PolynomialTrendForecaster\n",
    "from sktime.transformations.series.boxcox import LogTransformer\n",
    "from sktime.transformations.series.detrend import Deseasonalizer\n",
    "\n",
    "y = load_airline()\n",
    "\n",
    "f = LogTransformer() * Deseasonalizer(sp=12) * PolynomialTrendForecaster(degree=2)\n",
    "\n",
    "f.fit(y, fh=list(range(1, 13)))\n",
    "y_fcst = f.predict()\n",
    "\n",
    "plot_series(y, y_fcst)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what happened here?\n",
    "\n",
    "The \"chain\" operator `*` creates a \"forecasting pipeline\"\n",
    "\n",
    "Has the same interface as all other forecasters! No additional data fiddling!\n",
    "\n",
    "Transformers \"slot in\" as standardized components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
       "                                   PolynomialTrendForecaster(degree=2)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
       "                                   PolynomialTrendForecaster(degree=2)])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
       "                                   PolynomialTrendForecaster(degree=2)])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at this in more detail:\n",
    "\n",
    "* `sktime` transformers interface\n",
    "* `sktime` pipeline building"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Transformers - interface and features <a class=\"anchor\" id=\"section_3_2\"></a>\n",
    "\n",
    "* transformer interface\n",
    "* transformer types\n",
    "* searching transformers by type\n",
    "* broadcasting/vectorization to panel & hierarchical data\n",
    "* transformers and pipelines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 What are transformers? <a class=\"anchor\" id=\"section_3_2_1\"></a>\n",
    "\n",
    "Transformer = modular data processing steps commonly used in machine learning\n",
    "\n",
    "(\"transformer\" used in the sense of `scikit-learn`)\n",
    "\n",
    "Transformers are estimators that:\n",
    "\n",
    "* are fitted to a batch of data via `fit(data)`, changing its state\n",
    "* are applied to another batch of data via `transform(X)`, producing transformed data\n",
    "* may have an `inverse_transform(X)`\n",
    "\n",
    "In `sktime`, input `X` to `fit` and `transform` is typically a time series or a panel (collection of time series)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic use of an `sktime` time series transformer is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000-01-01    4.708975\n",
       " 2000-01-02    1.803052\n",
       " 2000-01-03    2.403074\n",
       " 2000-01-04    3.076577\n",
       " 2000-01-05    2.902616\n",
       " 2000-01-06    3.831219\n",
       " 2000-01-07    2.121627\n",
       " Freq: D, dtype: float64,\n",
       " 2000-01-08    4.858755\n",
       " 2000-01-09    3.460329\n",
       " 2000-01-10    2.280978\n",
       " 2000-01-11    1.930733\n",
       " 2000-01-12    4.604839\n",
       " Freq: D, dtype: float64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. prepare the data\n",
    "from sktime.utils._testing.series import _make_series\n",
    "\n",
    "X = _make_series()\n",
    "X_train = X[:7]\n",
    "X_test = X[7:12]\n",
    "# X_train and X_test are both pandas.Series\n",
    "\n",
    "X_train, X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. construct the transformer\n",
    "from sktime.transformations.series.boxcox import BoxCoxTransformer\n",
    "\n",
    "# trafo is an sktime estimator inheriting from BaseTransformer\n",
    "# Box-Cox transform with lambda parameter fitted via mle\n",
    "trafo = BoxCoxTransformer(method=\"mle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-08    1.242107\n",
       "2000-01-09    1.025417\n",
       "2000-01-10    0.725243\n",
       "2000-01-11    0.593567\n",
       "2000-01-12    1.209380\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. fit the transformer to training data\n",
    "trafo.fit(X_train)\n",
    "\n",
    "# 4. apply the transformer to transform test data\n",
    "# Box-Cox transform with lambda fitted on X_train\n",
    "X_transformed = trafo.transform(X_test)\n",
    "\n",
    "X_transformed"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the training and test set is the same, step 3 and 4 can be carried out more concisely (and sometimes more efficiently) by using `fit_transform`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3+4. apply the transformer to fit and transform on the same data, X\n",
    "X_transformed = trafo.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2 Different types of transformers <a class=\"anchor\" id=\"section_3_2_2\"></a>\n",
    "\n",
    "`sktime` distinguishes different types of transformer, depending on the input type of `fit` and `transform`, and the output type of `transform`.\n",
    "\n",
    "Transformers differ by:\n",
    "\n",
    "* making use of an additional `y` argument in `fit` or `transform`\n",
    "* whether the input to `fit` and `transform` is a single time series, a collection of time series, or scalar values (data frame row)\n",
    "* whether the output of `transform` is a single time series, a collection of time series, or scalar values (data frame row)\n",
    "* whether the input to `fit` and `transform` are one object or two. Two objects as input and a scalar output means the transformer is a distance or kernel function."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More detail on this is given in the glossary (section 2.3).\n",
    "\n",
    "To illustrate the difference, we compare two transformers with different output:\n",
    "\n",
    "* the Box-Cox transformer `BoxCoxTrannsformer`, which transforms a time series to a time series\n",
    "* the summary transformer `SummaryTransformer`, which transforms a time series to scalars such as the mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing the transformer\n",
    "from sktime.transformations.series.boxcox import BoxCoxTransformer\n",
    "from sktime.transformations.series.summarize import SummaryTransformer\n",
    "from sktime.utils._testing.series import _make_series\n",
    "\n",
    "# getting some data\n",
    "# this is one pandas.Series\n",
    "X = _make_series(n_timepoints=10)\n",
    "\n",
    "# constructing the transformers\n",
    "boxcox_trafo = BoxCoxTransformer(method=\"mle\")\n",
    "summary_trafo = SummaryTransformer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    3.217236\n",
       "2000-01-02    6.125564\n",
       "2000-01-03    5.264381\n",
       "2000-01-04    3.811121\n",
       "2000-01-05    1.966839\n",
       "2000-01-06    2.621609\n",
       "2000-01-07    3.851400\n",
       "2000-01-08    3.199416\n",
       "2000-01-09    0.000000\n",
       "2000-01-10    6.629380\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this produces a pandas Series\n",
    "boxcox_trafo.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.368131</td>\n",
       "      <td>1.128705</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.881081</td>\n",
       "      <td>2.339681</td>\n",
       "      <td>2.963718</td>\n",
       "      <td>3.376426</td>\n",
       "      <td>4.0816</td>\n",
       "      <td>4.67824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean       std  min       max       0.1      0.25       0.5    0.75  \\\n",
       "0  3.368131  1.128705  1.0  4.881081  2.339681  2.963718  3.376426  4.0816   \n",
       "\n",
       "       0.9  \n",
       "0  4.67824  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this produces a pandas.DataFrame row\n",
    "summary_trafo.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For time series transformers, the metadata tags describe the expected output of `transform`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Series'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxcox_trafo.get_tag(\"scitype:transform-output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Primitives'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_trafo.get_tag(\"scitype:transform-output\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find transformers, use `all_estimators` and filter by tags:\n",
    "\n",
    "* `\"scitype:transform-output\"` - the output scitype. `Series` for time series, `Primitives` for primitive features (float, categories), `Panel` for collections of time series.\n",
    "* `\"scitype:transform-input\"` - the input scitype. `Series` for time series.\n",
    "* `\"scitype:instancewise\"` - If `True`, vectorized operation per series. If `False`, uses multiple time series non-trivially."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: find all transformers that output time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Importing plotly failed. Interactive plots will not work.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>estimator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aggregator</td>\n",
       "      <td>&lt;class 'sktime.transformations.hierarchical.ag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AutoCorrelationTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.acf.Auto...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BoxCoxTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.boxcox.B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ClaSPTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.clasp.Cl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ClearSky</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.clear_sk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>TransformerPipeline</td>\n",
       "      <td>&lt;class 'sktime.transformations.compose.Transfo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>TruncationTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.truncatio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>WhiteNoiseAugmenter</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.augmente...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>WindowSummarizer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.summariz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>YtoX</td>\n",
       "      <td>&lt;class 'sktime.transformations.compose.YtoX'&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  \\\n",
       "0                   Aggregator   \n",
       "1   AutoCorrelationTransformer   \n",
       "2            BoxCoxTransformer   \n",
       "3             ClaSPTransformer   \n",
       "4                     ClearSky   \n",
       "..                         ...   \n",
       "69         TransformerPipeline   \n",
       "70       TruncationTransformer   \n",
       "71         WhiteNoiseAugmenter   \n",
       "72            WindowSummarizer   \n",
       "73                        YtoX   \n",
       "\n",
       "                                            estimator  \n",
       "0   <class 'sktime.transformations.hierarchical.ag...  \n",
       "1   <class 'sktime.transformations.series.acf.Auto...  \n",
       "2   <class 'sktime.transformations.series.boxcox.B...  \n",
       "3   <class 'sktime.transformations.series.clasp.Cl...  \n",
       "4   <class 'sktime.transformations.series.clear_sk...  \n",
       "..                                                ...  \n",
       "69  <class 'sktime.transformations.compose.Transfo...  \n",
       "70  <class 'sktime.transformations.panel.truncatio...  \n",
       "71  <class 'sktime.transformations.series.augmente...  \n",
       "72  <class 'sktime.transformations.series.summariz...  \n",
       "73      <class 'sktime.transformations.compose.YtoX'>  \n",
       "\n",
       "[74 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.registry import all_estimators\n",
    "\n",
    "# now subset to transformers that extract scalar features\n",
    "all_estimators(\n",
    "    \"transformer\",\n",
    "    as_dataframe=True,\n",
    "    filter_tags={\"scitype:transform-output\": \"Series\"},\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more complete overview on transformer types and tags is given in the `sktime` transformers tutorial.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.3 Broadcasting aka vectorization of transformers <a class=\"anchor\" id=\"section_3_2_3\"></a>\n",
    "\n",
    "`sktime` transformers may be natively univariate, or apply only to a single time series.\n",
    "\n",
    "Even if this is the case, they broadcast across variables and instances of time series, where applicable (also known as vectorization in `numpy` parlance).\n",
    "\n",
    "This ensures that all `sktime` transformers can be applied to multivariate and multi-instance (panel, hierarchical) time series data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 1: broadcasting/vectorization of time series to time series transformer\n",
    "\n",
    "The `BoxCoxTransformer` from previous sections applies to single instances of univariate time series. When multiple instances or variables are seen, it broadcasts across both:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>c0</th>\n",
       "      <th>c1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h0</th>\n",
       "      <th>h1</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">h0_0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">h1_0</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>3.068024</td>\n",
       "      <td>3.177475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>2.917533</td>\n",
       "      <td>3.615065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>3.654595</td>\n",
       "      <td>3.327944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>2.848652</td>\n",
       "      <td>4.694433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>3.458690</td>\n",
       "      <td>3.349914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">h0_1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">h1_3</th>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>4.056444</td>\n",
       "      <td>3.726508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>2.462253</td>\n",
       "      <td>3.938115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>2.689640</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>1.233706</td>\n",
       "      <td>3.999155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>3.101318</td>\n",
       "      <td>3.632666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            c0        c1\n",
       "h0   h1   time                          \n",
       "h0_0 h1_0 2000-01-01  3.068024  3.177475\n",
       "          2000-01-02  2.917533  3.615065\n",
       "          2000-01-03  3.654595  3.327944\n",
       "          2000-01-04  2.848652  4.694433\n",
       "          2000-01-05  3.458690  3.349914\n",
       "...                        ...       ...\n",
       "h0_1 h1_3 2000-01-08  4.056444  3.726508\n",
       "          2000-01-09  2.462253  3.938115\n",
       "          2000-01-10  2.689640  1.000000\n",
       "          2000-01-11  1.233706  3.999155\n",
       "          2000-01-12  3.101318  3.632666\n",
       "\n",
       "[96 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.transformations.series.boxcox import BoxCoxTransformer\n",
    "from sktime.utils._testing.hierarchical import _make_hierarchical\n",
    "\n",
    "# hierarchical data with 2 variables and 2 levels\n",
    "X = _make_hierarchical(n_columns=2)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">h0_0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">h1_0</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.307301</td>\n",
       "      <td>3.456645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>0.305723</td>\n",
       "      <td>4.416187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>0.311191</td>\n",
       "      <td>3.777609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.304881</td>\n",
       "      <td>7.108861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.310189</td>\n",
       "      <td>3.825267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">h0_1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">h1_3</th>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>1.884165</td>\n",
       "      <td>9.828613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>1.087370</td>\n",
       "      <td>11.311330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>1.216886</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>0.219210</td>\n",
       "      <td>11.761224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>1.435712</td>\n",
       "      <td>9.208733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            c0         c1\n",
       "h0   h1   time                           \n",
       "h0_0 h1_0 2000-01-01  0.307301   3.456645\n",
       "          2000-01-02  0.305723   4.416187\n",
       "          2000-01-03  0.311191   3.777609\n",
       "          2000-01-04  0.304881   7.108861\n",
       "          2000-01-05  0.310189   3.825267\n",
       "...                        ...        ...\n",
       "h0_1 h1_3 2000-01-08  1.884165   9.828613\n",
       "          2000-01-09  1.087370  11.311330\n",
       "          2000-01-10  1.216886   0.000000\n",
       "          2000-01-11  0.219210  11.761224\n",
       "          2000-01-12  1.435712   9.208733\n",
       "\n",
       "[96 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# constructing the transformers\n",
    "boxcox_trafo = BoxCoxTransformer(method=\"mle\")\n",
    "\n",
    "# applying to X results in hierarchical data\n",
    "boxcox_trafo.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitted model components of vectorized transformers can be found in the `transformers_` attribute, or accessed via the universal `get_fitted_params` interface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <td>BoxCoxTransformer()</td>\n",
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       "      <td>BoxCoxTransformer()</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                            c0                   c1\n",
       "h0   h1                                            \n",
       "h0_0 h1_0  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "     h1_1  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "     h1_2  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "     h1_3  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "h0_1 h1_0  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "     h1_1  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "     h1_2  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "     h1_3  BoxCoxTransformer()  BoxCoxTransformer()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxcox_trafo.transformers_\n",
    "# this is a pandas.DataFrame that contains the fitted transformers\n",
    "# one per time series instance and variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'transformers':                             c0                   c1\n",
       " h0   h1                                            \n",
       " h0_0 h1_0  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "      h1_1  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "      h1_2  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "      h1_3  BoxCoxTransformer()  BoxCoxTransformer()\n",
       " h0_1 h1_0  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "      h1_1  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "      h1_2  BoxCoxTransformer()  BoxCoxTransformer()\n",
       "      h1_3  BoxCoxTransformer()  BoxCoxTransformer(),\n",
       " \"transformers.loc[('h0_0', 'h1_0'),c0]\": BoxCoxTransformer(),\n",
       " \"transformers.loc[('h0_0', 'h1_0'),c0]__lambda\": -3.1599525634239187,\n",
       " \"transformers.loc[('h0_0', 'h1_1'),c1]\": BoxCoxTransformer(),\n",
       " \"transformers.loc[('h0_0', 'h1_1'),c1]__lambda\": 0.37511296223989965}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxcox_trafo.get_fitted_params()\n",
    "# this returns a dictionary\n",
    "# the transformers DataFrame is available at the key \"transformers\"\n",
    "# individual transformers are available at dataframe-like keys\n",
    "# it also contains all fitted lambdas as keyed parameters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 2: broadcasting/vectorization of time series to scalar features transformer\n",
    "\n",
    "The `SummaryTransformer` behaves similarly.\n",
    "Multiple time series instances are transformed to different columns of the resulting data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.644124</td>\n",
       "      <td>4.535796</td>\n",
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       "      <td>3.649374</td>\n",
       "      <td>1.181054</td>\n",
       "      <td>1.422356</td>\n",
       "      <td>5.359634</td>\n",
       "      <td>2.249409</td>\n",
       "      <td>2.881057</td>\n",
       "      <td>3.813969</td>\n",
       "      <td>4.319322</td>\n",
       "      <td>5.021987</td>\n",
       "      <td>2.945555</td>\n",
       "      <td>1.245355</td>\n",
       "      <td>1.684464</td>\n",
       "      <td>6.469536</td>\n",
       "      <td>1.795508</td>\n",
       "      <td>2.324243</td>\n",
       "      <td>2.757053</td>\n",
       "      <td>3.159779</td>\n",
       "      <td>3.547420</td>\n",
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       "      <td>2.313490</td>\n",
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       "      <td>2.839630</td>\n",
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       "      <td>3.372838</td>\n",
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       "      <td>3.259750</td>\n",
       "      <td>4.192159</td>\n",
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       "      <td>5.159135</td>\n",
       "      <td>1.933525</td>\n",
       "      <td>2.375844</td>\n",
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       "      <td>3.544128</td>\n",
       "      <td>3.954901</td>\n",
       "      <td>4.046171</td>\n",
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       "      <th>h1_1</th>\n",
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       "      <td>3.434244</td>\n",
       "      <td>3.799033</td>\n",
       "      <td>4.167242</td>\n",
       "      <td>3.116279</td>\n",
       "      <td>0.604060</td>\n",
       "      <td>2.235531</td>\n",
       "      <td>4.167924</td>\n",
       "      <td>2.426392</td>\n",
       "      <td>2.655720</td>\n",
       "      <td>3.079178</td>\n",
       "      <td>3.660901</td>\n",
       "      <td>3.762036</td>\n",
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       "    <tr>\n",
       "      <th>h1_2</th>\n",
       "      <td>3.397527</td>\n",
       "      <td>0.630344</td>\n",
       "      <td>2.277090</td>\n",
       "      <td>4.571272</td>\n",
       "      <td>2.791987</td>\n",
       "      <td>2.965040</td>\n",
       "      <td>3.457581</td>\n",
       "      <td>3.783002</td>\n",
       "      <td>4.031893</td>\n",
       "      <td>3.297039</td>\n",
       "      <td>0.938834</td>\n",
       "      <td>1.826276</td>\n",
       "      <td>4.919249</td>\n",
       "      <td>2.292343</td>\n",
       "      <td>2.646870</td>\n",
       "      <td>3.139703</td>\n",
       "      <td>3.975298</td>\n",
       "      <td>4.365553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1_3</th>\n",
       "      <td>3.356722</td>\n",
       "      <td>1.326547</td>\n",
       "      <td>1.233706</td>\n",
       "      <td>5.505544</td>\n",
       "      <td>2.467667</td>\n",
       "      <td>2.567089</td>\n",
       "      <td>2.884737</td>\n",
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       "      <td>2.568151</td>\n",
       "      <td>3.659943</td>\n",
       "      <td>3.953375</td>\n",
       "      <td>4.022143</td>\n",
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       "           c0__mean   c0__std   c0__min   c0__max   c0__0.1  c0__0.25  \\\n",
       "h0   h1                                                                 \n",
       "h0_0 h1_0  3.202174  0.732349  2.498101  5.283440  2.709206  2.834797   \n",
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       "     h1_3  2.865339  0.745604  1.654998  4.718420  2.313490  2.477173   \n",
       "h0_1 h1_0  2.946692  1.025167  1.085568  5.159135  1.933525  2.375844   \n",
       "     h1_1  3.274710  0.883594  1.930773  4.771649  1.988411  2.710401   \n",
       "     h1_2  3.397527  0.630344  2.277090  4.571272  2.791987  2.965040   \n",
       "     h1_3  3.356722  1.326547  1.233706  5.505544  2.467667  2.567089   \n",
       "\n",
       "            c0__0.5  c0__0.75   c0__0.9  c1__mean   c1__std   c1__min  \\\n",
       "h0   h1                                                                 \n",
       "h0_0 h1_0  2.975883  3.348140  3.635005  3.360042  0.744295  1.910203   \n",
       "     h1_1  2.742309  3.084133  3.349082  3.637274  1.006419  2.376048   \n",
       "     h1_2  3.813969  4.319322  5.021987  2.945555  1.245355  1.684464   \n",
       "     h1_3  2.839630  3.137472  3.372838  3.394633  0.971250  1.866518   \n",
       "h0_1 h1_0  2.952310  3.412478  3.687086  3.203431  0.970914  1.554428   \n",
       "     h1_1  3.434244  3.799033  4.167242  3.116279  0.604060  2.235531   \n",
       "     h1_2  3.457581  3.783002  4.031893  3.297039  0.938834  1.826276   \n",
       "     h1_3  2.884737  4.308726  5.273261  3.232578  1.003957  1.000000   \n",
       "\n",
       "            c1__max   c1__0.1  c1__0.25   c1__0.5  c1__0.75   c1__0.9  \n",
       "h0   h1                                                                \n",
       "h0_0 h1_0  4.694433  2.278782  3.194950  3.377147  3.722876  3.981182  \n",
       "     h1_1  5.112509  2.402845  2.703573  3.644124  4.535796  4.873311  \n",
       "     h1_2  6.469536  1.795508  2.324243  2.757053  3.159779  3.547420  \n",
       "     h1_3  5.236633  2.506371  2.653524  3.259750  4.192159  4.419325  \n",
       "h0_1 h1_0  4.546142  1.756260  2.405147  3.544128  3.954901  4.046171  \n",
       "     h1_1  4.167924  2.426392  2.655720  3.079178  3.660901  3.762036  \n",
       "     h1_2  4.919249  2.292343  2.646870  3.139703  3.975298  4.365553  \n",
       "     h1_3  4.234051  2.113028  2.568151  3.659943  3.953375  4.022143  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.transformations.series.summarize import SummaryTransformer\n",
    "\n",
    "summary_trafo = SummaryTransformer()\n",
    "\n",
    "# this produces a pandas DataFrame with more rows and columns\n",
    "# rows correspond to different instances in X\n",
    "# columns are multiplied and names prefixed by [variablename]__\n",
    "# there is one column per variable and transformed feature\n",
    "summary_trafo.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.4 Transformers as pipeline components <a class=\"anchor\" id=\"section_3_2_4\"></a>\n",
    "\n",
    "`sktime` transformers can be pipelined with any other `sktime` estimator type, including forecasters, classifiers, and other transformers.\n",
    "\n",
    "Pipelines = estimators of the same type, same interface as specialized class\n",
    "\n",
    "pipeline build operation: `make_pipeline` or via `*` dunder\n",
    "\n",
    "Pipelining `pipe = trafo * est` produces `pipe` of same type as `est`.\n",
    "\n",
    "In `pipe.fit`, first `trafo.fit_transform`, then `est.fit` is executed on the result.\n",
    "\n",
    "In `pipe.predict`, first `trafo.transform`, then `est.predict` is executed.\n",
    "\n",
    "(the arguments that are piped differ by type and can be looked up in the docstrings of pipeline classes, or specialized tutorials)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 1: forecaster pipeline\n",
    "\n",
    "we have seen this example above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
       "                                   PolynomialTrendForecaster(degree=2)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
       "                                   PolynomialTrendForecaster(degree=2)])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
       "                                   PolynomialTrendForecaster(degree=2)])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.forecasting.trend import PolynomialTrendForecaster\n",
    "from sktime.transformations.series.boxcox import LogTransformer\n",
    "from sktime.transformations.series.detrend import Deseasonalizer\n",
    "\n",
    "y = load_airline()\n",
    "\n",
    "pipe = LogTransformer() * Deseasonalizer(sp=12) * PolynomialTrendForecaster(degree=2)\n",
    "\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>,\n",
       " <AxesSubplot: ylabel='Number of airline passengers'>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is a forecaster with the same interface as Polynomial Trend Forecaster\n",
    "pipe.fit(y, fh=[1, 2, 3])\n",
    "y_pred = pipe.predict()\n",
    "\n",
    "plot_series(y, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 2: classifier pipeline\n",
    "\n",
    "works the same with classifiers or other estimator types!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ClassifierPipeline(classifier=KNeighborsTimeSeriesClassifier(),\n",
       "                   transformers=[ExponentTransformer()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClassifierPipeline</label><div class=\"sk-toggleable__content\"><pre>ClassifierPipeline(classifier=KNeighborsTimeSeriesClassifier(),\n",
       "                   transformers=[ExponentTransformer()])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">classifier: KNeighborsTimeSeriesClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsTimeSeriesClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsTimeSeriesClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsTimeSeriesClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ClassifierPipeline(classifier=KNeighborsTimeSeriesClassifier(),\n",
       "                   transformers=[ExponentTransformer()])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier\n",
    "from sktime.transformations.series.exponent import ExponentTransformer\n",
    "\n",
    "pipe = ExponentTransformer() * KNeighborsTimeSeriesClassifier()\n",
    "\n",
    "# this constructs a ClassifierPipeline, which is also a classifier\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ClassifierPipeline(classifier=KNeighborsTimeSeriesClassifier(),\n",
       "                   transformers=[ExponentTransformer()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClassifierPipeline</label><div class=\"sk-toggleable__content\"><pre>ClassifierPipeline(classifier=KNeighborsTimeSeriesClassifier(),\n",
       "                   transformers=[ExponentTransformer()])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">classifier: KNeighborsTimeSeriesClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsTimeSeriesClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsTimeSeriesClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsTimeSeriesClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ClassifierPipeline(classifier=KNeighborsTimeSeriesClassifier(),\n",
       "                   transformers=[ExponentTransformer()])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.datasets import load_unit_test\n",
    "\n",
    "X_train, y_train = load_unit_test(split=\"TRAIN\")\n",
    "X_test, _ = load_unit_test(split=\"TEST\")\n",
    "\n",
    "# this is a forecaster with the same interface as knn-classifier\n",
    "# first applies exponent transform, then knn-classifier\n",
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Combining transformers, feature engineering <a class=\"anchor\" id=\"section_3_3\"></a>\n",
    "\n",
    "transformers are natural pipeline components\n",
    "\n",
    "* data processing steps\n",
    "* feature engineering steps\n",
    "* post processing steps\n",
    "\n",
    "they can be combined in a number of other ways:\n",
    "\n",
    "* pipelining = sequential chaining\n",
    "* feature union = parallel, addition of features\n",
    "* feature subsetting = selecting columns\n",
    "* inversion = switch transform and inverse\n",
    "* multiplexing = switching between transformers\n",
    "* passthrough = switch on/ off"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chaining transformers via `*`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformerPipeline(steps=[Differencer(), SummaryTransformer()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TransformerPipeline</label><div class=\"sk-toggleable__content\"><pre>TransformerPipeline(steps=[Differencer(), SummaryTransformer()])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TransformerPipeline(steps=[Differencer(), SummaryTransformer()])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.transformations.series.difference import Differencer\n",
    "from sktime.transformations.series.summarize import SummaryTransformer\n",
    "\n",
    "pipe = Differencer() * SummaryTransformer()\n",
    "\n",
    "# this constructs a TransformerPipeline, which is also a transformer\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.222222</td>\n",
       "      <td>33.636569</td>\n",
       "      <td>-101.0</td>\n",
       "      <td>87.00</td>\n",
       "      <td>-37.700</td>\n",
       "      <td>-16.000</td>\n",
       "      <td>3.50</td>\n",
       "      <td>22.25</td>\n",
       "      <td>43.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.111111</td>\n",
       "      <td>810.876526</td>\n",
       "      <td>-2680.3</td>\n",
       "      <td>2416.86</td>\n",
       "      <td>-826.462</td>\n",
       "      <td>-323.145</td>\n",
       "      <td>76.33</td>\n",
       "      <td>448.86</td>\n",
       "      <td>1021.974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean         std     min      max      0.1     0.25    0.5    0.75  \\\n",
       "0   2.222222   33.636569  -101.0    87.00  -37.700  -16.000   3.50   22.25   \n",
       "1  48.111111  810.876526 -2680.3  2416.86 -826.462 -323.145  76.33  448.86   \n",
       "\n",
       "        0.9  \n",
       "0    43.000  \n",
       "1  1021.974  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.utils._testing.hierarchical import _bottom_hier_datagen\n",
    "\n",
    "X = _bottom_hier_datagen(no_levels=1, no_bottom_nodes=2)\n",
    "\n",
    "# this is a transformer with the same interface\n",
    "# first applies differencer, then summary transform\n",
    "pipe.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compatible with sklearn transformers!\n",
    "\n",
    "default applies sklearn transformer per individual time series as a data frame table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformerPipeline(steps=[Differencer(),\n",
       "                           TabularToSeriesAdaptor(transformer=StandardScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TransformerPipeline</label><div class=\"sk-toggleable__content\"><pre>TransformerPipeline(steps=[Differencer(),\n",
       "                           TabularToSeriesAdaptor(transformer=StandardScaler())])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TransformerPipeline(steps=[Differencer(),\n",
       "                           TabularToSeriesAdaptor(transformer=StandardScaler())])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "pipe = Differencer() * StandardScaler()\n",
    "\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_agg</th>\n",
       "      <th>timepoints</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">l1_node01</th>\n",
       "      <th>1949-01</th>\n",
       "      <td>-0.066296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02</th>\n",
       "      <td>0.112704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03</th>\n",
       "      <td>0.351370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04</th>\n",
       "      <td>-0.155796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05</th>\n",
       "      <td>-0.304963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">l1_node02</th>\n",
       "      <th>1960-08</th>\n",
       "      <td>-0.623659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09</th>\n",
       "      <td>-3.376512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10</th>\n",
       "      <td>-1.565994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11</th>\n",
       "      <td>-2.231567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12</th>\n",
       "      <td>1.210249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      passengers\n",
       "l1_agg    timepoints            \n",
       "l1_node01 1949-01      -0.066296\n",
       "          1949-02       0.112704\n",
       "          1949-03       0.351370\n",
       "          1949-04      -0.155796\n",
       "          1949-05      -0.304963\n",
       "...                          ...\n",
       "l1_node02 1960-08      -0.623659\n",
       "          1960-09      -3.376512\n",
       "          1960-10      -1.565994\n",
       "          1960-11      -2.231567\n",
       "          1960-12       1.210249\n",
       "\n",
       "[288 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pipeline-adaptor chains can be constructed manually:\n",
    "\n",
    "* `sktime.transformations.compose.TransformerPipeline`\n",
    "* `sktime.transformations.series.adapt.TabularToSeriesAdaptor` for `sklearn`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "composites are compatible with `get_params` / `set_params` parameter interface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'steps': [Differencer(),\n",
       "  TabularToSeriesAdaptor(transformer=StandardScaler())],\n",
       " 'Differencer': Differencer(),\n",
       " 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()),\n",
       " 'Differencer__lags': 1,\n",
       " 'Differencer__memory': 'all',\n",
       " 'Differencer__na_handling': 'fill_zero',\n",
       " 'TabularToSeriesAdaptor__fit_in_transform': False,\n",
       " 'TabularToSeriesAdaptor__transformer__copy': True,\n",
       " 'TabularToSeriesAdaptor__transformer__with_mean': True,\n",
       " 'TabularToSeriesAdaptor__transformer__with_std': True,\n",
       " 'TabularToSeriesAdaptor__transformer': StandardScaler()}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.get_params()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature union via `+`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>FeatureUnion(transformer_list=[Differencer(), Lag()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FeatureUnion</label><div class=\"sk-toggleable__content\"><pre>FeatureUnion(transformer_list=[Differencer(), Lag()])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "FeatureUnion(transformer_list=[Differencer(), Lag()])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.transformations.series.difference import Differencer\n",
    "from sktime.transformations.series.lag import Lag\n",
    "\n",
    "pipe = Differencer() + Lag()\n",
    "\n",
    "# this constructs a FeatureUnion, which is also a transformer\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Differencer__passengers</th>\n",
       "      <th>Lag__lag_0__passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_agg</th>\n",
       "      <th>timepoints</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">l1_node01</th>\n",
       "      <th>1949-01</th>\n",
       "      <td>0.00</td>\n",
       "      <td>112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02</th>\n",
       "      <td>6.00</td>\n",
       "      <td>118.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03</th>\n",
       "      <td>14.00</td>\n",
       "      <td>132.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04</th>\n",
       "      <td>-3.00</td>\n",
       "      <td>129.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05</th>\n",
       "      <td>-8.00</td>\n",
       "      <td>121.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">l1_node02</th>\n",
       "      <th>1960-08</th>\n",
       "      <td>-1920.80</td>\n",
       "      <td>38845.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09</th>\n",
       "      <td>-10759.42</td>\n",
       "      <td>28085.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10</th>\n",
       "      <td>-4546.78</td>\n",
       "      <td>23539.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11</th>\n",
       "      <td>-6114.52</td>\n",
       "      <td>17424.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12</th>\n",
       "      <td>3507.42</td>\n",
       "      <td>20931.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Differencer__passengers  Lag__lag_0__passengers\n",
       "l1_agg    timepoints                                                 \n",
       "l1_node01 1949-01                        0.00                  112.00\n",
       "          1949-02                        6.00                  118.00\n",
       "          1949-03                       14.00                  132.00\n",
       "          1949-04                       -3.00                  129.00\n",
       "          1949-05                       -8.00                  121.00\n",
       "...                                       ...                     ...\n",
       "l1_node02 1960-08                    -1920.80                38845.27\n",
       "          1960-09                   -10759.42                28085.85\n",
       "          1960-10                    -4546.78                23539.07\n",
       "          1960-11                    -6114.52                17424.55\n",
       "          1960-12                     3507.42                20931.97\n",
       "\n",
       "[288 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.utils._testing.hierarchical import _bottom_hier_datagen\n",
    "\n",
    "X = _bottom_hier_datagen(no_levels=1, no_bottom_nodes=2)\n",
    "\n",
    "# applies both Differencer and Lag, returns transformed in different columns\n",
    "pipe.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to retain the original columns, use the `Id` transformer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Id__passengers</th>\n",
       "      <th>Differencer__passengers</th>\n",
       "      <th>Lag__lag_1__passengers</th>\n",
       "      <th>Lag__lag_2__passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_agg</th>\n",
       "      <th>timepoints</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">l1_node01</th>\n",
       "      <th>1949-01</th>\n",
       "      <td>112.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02</th>\n",
       "      <td>118.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03</th>\n",
       "      <td>132.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>118.00</td>\n",
       "      <td>112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04</th>\n",
       "      <td>129.00</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>132.00</td>\n",
       "      <td>118.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05</th>\n",
       "      <td>121.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>129.00</td>\n",
       "      <td>132.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">l1_node02</th>\n",
       "      <th>1960-08</th>\n",
       "      <td>38845.27</td>\n",
       "      <td>-1920.80</td>\n",
       "      <td>40766.07</td>\n",
       "      <td>30877.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09</th>\n",
       "      <td>28085.85</td>\n",
       "      <td>-10759.42</td>\n",
       "      <td>38845.27</td>\n",
       "      <td>40766.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10</th>\n",
       "      <td>23539.07</td>\n",
       "      <td>-4546.78</td>\n",
       "      <td>28085.85</td>\n",
       "      <td>38845.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11</th>\n",
       "      <td>17424.55</td>\n",
       "      <td>-6114.52</td>\n",
       "      <td>23539.07</td>\n",
       "      <td>28085.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12</th>\n",
       "      <td>20931.97</td>\n",
       "      <td>3507.42</td>\n",
       "      <td>17424.55</td>\n",
       "      <td>23539.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Id__passengers  Differencer__passengers  \\\n",
       "l1_agg    timepoints                                            \n",
       "l1_node01 1949-01             112.00                     0.00   \n",
       "          1949-02             118.00                     6.00   \n",
       "          1949-03             132.00                    14.00   \n",
       "          1949-04             129.00                    -3.00   \n",
       "          1949-05             121.00                    -8.00   \n",
       "...                              ...                      ...   \n",
       "l1_node02 1960-08           38845.27                 -1920.80   \n",
       "          1960-09           28085.85                -10759.42   \n",
       "          1960-10           23539.07                 -4546.78   \n",
       "          1960-11           17424.55                 -6114.52   \n",
       "          1960-12           20931.97                  3507.42   \n",
       "\n",
       "                      Lag__lag_1__passengers  Lag__lag_2__passengers  \n",
       "l1_agg    timepoints                                                  \n",
       "l1_node01 1949-01                        NaN                     NaN  \n",
       "          1949-02                     112.00                     NaN  \n",
       "          1949-03                     118.00                  112.00  \n",
       "          1949-04                     132.00                  118.00  \n",
       "          1949-05                     129.00                  132.00  \n",
       "...                                      ...                     ...  \n",
       "l1_node02 1960-08                   40766.07                30877.65  \n",
       "          1960-09                   38845.27                40766.07  \n",
       "          1960-10                   28085.85                38845.27  \n",
       "          1960-11                   23539.07                28085.85  \n",
       "          1960-12                   17424.55                23539.07  \n",
       "\n",
       "[288 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.transformations.compose import Id\n",
    "from sktime.transformations.series.difference import Differencer\n",
    "from sktime.transformations.series.lag import Lag\n",
    "\n",
    "pipe = Id() + Differencer() + Lag([1, 2], index_out=\"original\")\n",
    "\n",
    "pipe.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'flatten_transform_index': True,\n",
       " 'n_jobs': None,\n",
       " 'transformer_list': [Id(),\n",
       "  Differencer(),\n",
       "  Lag(index_out='original', lags=[1, 2])],\n",
       " 'transformer_weights': None,\n",
       " 'Id': Id(),\n",
       " 'Differencer': Differencer(),\n",
       " 'Lag': Lag(index_out='original', lags=[1, 2]),\n",
       " 'Id___output_convert': 'auto',\n",
       " 'Differencer__lags': 1,\n",
       " 'Differencer__memory': 'all',\n",
       " 'Differencer__na_handling': 'fill_zero',\n",
       " 'Lag__flatten_transform_index': True,\n",
       " 'Lag__freq': None,\n",
       " 'Lag__index_out': 'original',\n",
       " 'Lag__keep_column_names': False,\n",
       " 'Lag__lags': [1, 2]}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameter inspection\n",
    "pipe.get_params()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subset input columns via `[colname]`\n",
    "\n",
    "let's say we want to apply `Differencer` to column 0, and `Lag` to column 1\n",
    "\n",
    "also we keep the original columns for illustration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>c0</th>\n",
       "      <th>c1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h0</th>\n",
       "      <th>h1</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">h0_0</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">h1_0</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>3.356766</td>\n",
       "      <td>2.649204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>2.262487</td>\n",
       "      <td>2.204119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>2.087692</td>\n",
       "      <td>2.186494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">h1_1</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>4.311237</td>\n",
       "      <td>3.129610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>3.190134</td>\n",
       "      <td>1.747807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>4.231399</td>\n",
       "      <td>2.483151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">h0_1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">h1_0</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>4.356575</td>\n",
       "      <td>3.550554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>2.865619</td>\n",
       "      <td>2.783107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>3.781770</td>\n",
       "      <td>2.619533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">h1_1</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>3.113704</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>2.673081</td>\n",
       "      <td>2.561047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.953516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            c0        c1\n",
       "h0   h1   time                          \n",
       "h0_0 h1_0 2000-01-01  3.356766  2.649204\n",
       "          2000-01-02  2.262487  2.204119\n",
       "          2000-01-03  2.087692  2.186494\n",
       "     h1_1 2000-01-01  4.311237  3.129610\n",
       "          2000-01-02  3.190134  1.747807\n",
       "          2000-01-03  4.231399  2.483151\n",
       "h0_1 h1_0 2000-01-01  4.356575  3.550554\n",
       "          2000-01-02  2.865619  2.783107\n",
       "          2000-01-03  3.781770  2.619533\n",
       "     h1_1 2000-01-01  3.113704  1.000000\n",
       "          2000-01-02  2.673081  2.561047\n",
       "          2000-01-03  1.000000  2.953516"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.utils._testing.hierarchical import _make_hierarchical\n",
    "\n",
    "X = _make_hierarchical(\n",
    "    hierarchy_levels=(2, 2), n_columns=2, min_timepoints=3, max_timepoints=3\n",
    ")\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <th>h0</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2000-01-02</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2000-01-02</th>\n",
       "      <td>3.190134</td>\n",
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       "      <td>-1.121103</td>\n",
       "      <td>3.129610</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>4.231399</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2000-01-02</th>\n",
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       "      <td>3.550554</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">h1_1</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>3.113704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2000-01-02</th>\n",
       "      <td>2.673081</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
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      ],
      "text/plain": [
       "                        Id__c0    Id__c1  TransformerPipeline_1__c0  \\\n",
       "h0   h1   time                                                        \n",
       "h0_0 h1_0 2000-01-01  3.356766  2.649204                   0.000000   \n",
       "          2000-01-02  2.262487  2.204119                  -1.094279   \n",
       "          2000-01-03  2.087692  2.186494                  -0.174795   \n",
       "     h1_1 2000-01-01  4.311237  3.129610                   0.000000   \n",
       "          2000-01-02  3.190134  1.747807                  -1.121103   \n",
       "          2000-01-03  4.231399  2.483151                   1.041265   \n",
       "h0_1 h1_0 2000-01-01  4.356575  3.550554                   0.000000   \n",
       "          2000-01-02  2.865619  2.783107                  -1.490956   \n",
       "          2000-01-03  3.781770  2.619533                   0.916151   \n",
       "     h1_1 2000-01-01  3.113704  1.000000                   0.000000   \n",
       "          2000-01-02  2.673081  2.561047                  -0.440623   \n",
       "          2000-01-03  1.000000  2.953516                  -1.673081   \n",
       "\n",
       "                      TransformerPipeline_2__lag_1__c1  \\\n",
       "h0   h1   time                                           \n",
       "h0_0 h1_0 2000-01-01                               NaN   \n",
       "          2000-01-02                          2.649204   \n",
       "          2000-01-03                          2.204119   \n",
       "     h1_1 2000-01-01                               NaN   \n",
       "          2000-01-02                          3.129610   \n",
       "          2000-01-03                          1.747807   \n",
       "h0_1 h1_0 2000-01-01                               NaN   \n",
       "          2000-01-02                          3.550554   \n",
       "          2000-01-03                          2.783107   \n",
       "     h1_1 2000-01-01                               NaN   \n",
       "          2000-01-02                          1.000000   \n",
       "          2000-01-03                          2.561047   \n",
       "\n",
       "                      TransformerPipeline_2__lag_2__c1  \n",
       "h0   h1   time                                          \n",
       "h0_0 h1_0 2000-01-01                               NaN  \n",
       "          2000-01-02                               NaN  \n",
       "          2000-01-03                          2.649204  \n",
       "     h1_1 2000-01-01                               NaN  \n",
       "          2000-01-02                               NaN  \n",
       "          2000-01-03                          3.129610  \n",
       "h0_1 h1_0 2000-01-01                               NaN  \n",
       "          2000-01-02                               NaN  \n",
       "          2000-01-03                          3.550554  \n",
       "     h1_1 2000-01-01                               NaN  \n",
       "          2000-01-02                               NaN  \n",
       "          2000-01-03                          1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.transformations.compose import Id\n",
    "from sktime.transformations.series.difference import Differencer\n",
    "from sktime.transformations.series.lag import Lag\n",
    "\n",
    "pipe = Id() + Differencer()[\"c0\"] + Lag([1, 2], index_out=\"original\")[\"c1\"]\n",
    "\n",
    "pipe.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "auto-generated names can be replaced by using `FeatureUnion` explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>original__c1</th>\n",
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       "      <th>lag__lag_1__c1</th>\n",
       "      <th>lag__lag_2__c0</th>\n",
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       "      <th>2000-01-03</th>\n",
       "      <td>4.231399</td>\n",
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       "      <td>1.041265</td>\n",
       "      <td>3.190134</td>\n",
       "      <td>1.747807</td>\n",
       "      <td>4.311237</td>\n",
       "      <td>3.129610</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">h0_1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">h1_0</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>4.356575</td>\n",
       "      <td>3.550554</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
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       "      <td>-1.490956</td>\n",
       "      <td>4.356575</td>\n",
       "      <td>3.550554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">h1_1</th>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>3.113704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>2.673081</td>\n",
       "      <td>2.561047</td>\n",
       "      <td>-0.440623</td>\n",
       "      <td>3.113704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.953516</td>\n",
       "      <td>-1.673081</td>\n",
       "      <td>2.673081</td>\n",
       "      <td>2.561047</td>\n",
       "      <td>3.113704</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      original__c0  original__c1  diff__c0  lag__lag_1__c0  \\\n",
       "h0   h1   time                                                               \n",
       "h0_0 h1_0 2000-01-01      3.356766      2.649204  0.000000             NaN   \n",
       "          2000-01-02      2.262487      2.204119 -1.094279        3.356766   \n",
       "          2000-01-03      2.087692      2.186494 -0.174795        2.262487   \n",
       "     h1_1 2000-01-01      4.311237      3.129610  0.000000             NaN   \n",
       "          2000-01-02      3.190134      1.747807 -1.121103        4.311237   \n",
       "          2000-01-03      4.231399      2.483151  1.041265        3.190134   \n",
       "h0_1 h1_0 2000-01-01      4.356575      3.550554  0.000000             NaN   \n",
       "          2000-01-02      2.865619      2.783107 -1.490956        4.356575   \n",
       "          2000-01-03      3.781770      2.619533  0.916151        2.865619   \n",
       "     h1_1 2000-01-01      3.113704      1.000000  0.000000             NaN   \n",
       "          2000-01-02      2.673081      2.561047 -0.440623        3.113704   \n",
       "          2000-01-03      1.000000      2.953516 -1.673081        2.673081   \n",
       "\n",
       "                      lag__lag_1__c1  lag__lag_2__c0  lag__lag_2__c1  \n",
       "h0   h1   time                                                        \n",
       "h0_0 h1_0 2000-01-01             NaN             NaN             NaN  \n",
       "          2000-01-02        2.649204             NaN             NaN  \n",
       "          2000-01-03        2.204119        3.356766        2.649204  \n",
       "     h1_1 2000-01-01             NaN             NaN             NaN  \n",
       "          2000-01-02        3.129610             NaN             NaN  \n",
       "          2000-01-03        1.747807        4.311237        3.129610  \n",
       "h0_1 h1_0 2000-01-01             NaN             NaN             NaN  \n",
       "          2000-01-02        3.550554             NaN             NaN  \n",
       "          2000-01-03        2.783107        4.356575        3.550554  \n",
       "     h1_1 2000-01-01             NaN             NaN             NaN  \n",
       "          2000-01-02        1.000000             NaN             NaN  \n",
       "          2000-01-03        2.561047        3.113704        1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.transformations.compose import FeatureUnion\n",
    "\n",
    "pipe = FeatureUnion(\n",
    "    [\n",
    "        (\"original\", Id()),\n",
    "        (\"diff\", Differencer()[\"c0\"]),\n",
    "        (\"lag\", Lag([1, 2], index_out=\"original\")),\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipe.fit_transform(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### turning log transform into exp transform via invert `~`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.71828183,  7.3890561 , 20.08553692])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sktime.transformations.series.boxcox import LogTransformer\n",
    "\n",
    "log = LogTransformer()\n",
    "\n",
    "exp = ~log\n",
    "\n",
    "# this behaves like an \"e to the power of\" transformer now\n",
    "exp.fit_transform(np.array([1, 2, 3]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### autoML structure compositors: multiplexer switch `¦` and on/off switch `-`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "expose decisions as parameter\n",
    "\n",
    "* do we want differencer *or* lag? for tuning later\n",
    "* do we want [differencer and lag] or [original features and lag] ? for tuning later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'selected_transformer': None,\n",
       " 'transformers': [Differencer(), Lag()],\n",
       " 'Differencer': Differencer(),\n",
       " 'Lag': Lag(),\n",
       " 'Differencer__lags': 1,\n",
       " 'Differencer__memory': 'all',\n",
       " 'Differencer__na_handling': 'fill_zero',\n",
       " 'Lag__flatten_transform_index': True,\n",
       " 'Lag__freq': None,\n",
       " 'Lag__index_out': 'extend',\n",
       " 'Lag__keep_column_names': False,\n",
       " 'Lag__lags': 0}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# differencer or lag\n",
    "\n",
    "from sktime.transformations.series.difference import Differencer\n",
    "from sktime.transformations.series.lag import Lag\n",
    "\n",
    "pipe = Differencer() | Lag()\n",
    "\n",
    "pipe.get_params()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the `selected_transformer` parameter exposes the choice:\n",
    "\n",
    "does this behave as `Lag` or `Differencer`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultiplexTransformer(selected_transformer=&#x27;Lag&#x27;,\n",
       "                     transformers=[Differencer(), Lag()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultiplexTransformer</label><div class=\"sk-toggleable__content\"><pre>MultiplexTransformer(selected_transformer=&#x27;Lag&#x27;,\n",
       "                     transformers=[Differencer(), Lag()])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultiplexTransformer(selected_transformer='Lag',\n",
       "                     transformers=[Differencer(), Lag()])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# switch = Lag -> this is a Lag transformer now!\n",
    "pipe.set_params(selected_transformer=\"Lag\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultiplexTransformer(selected_transformer=&#x27;Differencer&#x27;,\n",
       "                     transformers=[Differencer(), Lag()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultiplexTransformer</label><div class=\"sk-toggleable__content\"><pre>MultiplexTransformer(selected_transformer=&#x27;Differencer&#x27;,\n",
       "                     transformers=[Differencer(), Lag()])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultiplexTransformer(selected_transformer='Differencer',\n",
       "                     transformers=[Differencer(), Lag()])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# switch = Lag -> this is a Differencer now!\n",
    "pipe.set_params(selected_transformer=\"Differencer\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "similar, on/off switch with `~`\n",
    "\n",
    "same as multiplexer between wrapped transformer and `Id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OptionalPassthrough(transformer=Differencer())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OptionalPassthrough</label><div class=\"sk-toggleable__content\"><pre>OptionalPassthrough(transformer=Differencer())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: Differencer</label><div class=\"sk-toggleable__content\"><pre>Differencer()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Differencer</label><div class=\"sk-toggleable__content\"><pre>Differencer()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OptionalPassthrough(transformer=Differencer())"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optional_differencer = -Differencer()\n",
    "\n",
    "# this behaves as Differencer now\n",
    "optional_differencer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OptionalPassthrough(passthrough=True, transformer=Differencer())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OptionalPassthrough</label><div class=\"sk-toggleable__content\"><pre>OptionalPassthrough(passthrough=True, transformer=Differencer())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: Differencer</label><div class=\"sk-toggleable__content\"><pre>Differencer()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Differencer</label><div class=\"sk-toggleable__content\"><pre>Differencer()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OptionalPassthrough(passthrough=True, transformer=Differencer())"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is now just the identity transformer\n",
    "optional_differencer.set_params(passthrough=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Technical details - transformer types and signatures <a class=\"anchor\" id=\"section_3_4\"></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section explains the different types of transformers found in `sktime` in detail.\n",
    "\n",
    "There are four main types of transformation in `sktime`:\n",
    "\n",
    "* transforming a series/sequence into scalar- or category-valued features. Examples: `tsfresh`, or extracting `mean` and `variance` overall.\n",
    "* transforming a series into another series. Examples: detrending, smoothing, filtering, lagging.\n",
    "* transforming a panel into another panel. Examples: principal component projection; applying individual series-to-series transformation to all series in the panel.\n",
    "* transforming a pair of series into a scalar value. Examples: dynamic time warping distance between series/sequences; generalized alignment kernel between series/sequences.\n",
    "\n",
    "Notably, the first three (series to primitive features, series to series, panel to panel) are covered by the same base class template and module. We call these transformers \"time series transformers\", or, simply, \"transformers\".\n",
    "Kernels and distances for time series and sequences have the same mathematical signature and differ only in mathematical properties (e.g., definiteness assumptions) - they are covered by the more abstract scientific type of \"pairwise transformer\".\n",
    "\n",
    "Below, we give an overview in sub-sections:\n",
    "* reviewing common data container formats for series and panels\n",
    "* showcasing the signature of time series transformers, that transform a single series or panel\n",
    "* showcasing the signature of pairwise transformers, that transform a pair of series to a scalar, e.g., distances or kernels\n",
    "* how to search `sktime` for transformers of a certain type"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.1 Data container format<a class=\"anchor\" id=\"section_3_4_1\"></a>\n",
    "\n",
    "`sktime` transformers apply to individual time series and panels. Panels are collections of time series, and we refer to each time series in a panel as an \"instance\" of the panel.\n",
    "This is formalized as abstract \"scientific types\" `Series` and `Panel`, with multiple possible in-memory representations, so-called \"mtypes\".\n",
    "\n",
    "For the purpose of this tutorial, we will be working with the most common mtypes. For more details and formal data type specifications, see the \"datatypes and datasets\" tutorial.\n",
    "\n",
    "`Series` are commonly represented as:\n",
    "\n",
    "* `pandas.Series` for univariate time series and sequences\n",
    "* `pandas.DataFrame` for uni- or multivariate time series and sequences\n",
    "\n",
    "The `Series.index` and `DataFrame.index` are used for representing the time series or sequence index. `sktime` supports pandas integer, period and timestamp indices.\n",
    "\n",
    "`Panel`-s are commonly represented as:\n",
    "* a `pandas.DataFrame` in a specific format, defined by the `pd-multiindex` mtype. This has a 2-level index, for time points and instances\n",
    "* a `list` of `pandas.DataFrame`, where all `pandas.DataFrame` are in the `Series` format. The different `list` elements are the different instances\n",
    "\n",
    "In either case, the \"time\" index must be a `sktime` compatible time index type, as for `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datatypes import get_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:05.849641Z",
     "iopub.status.busy": "2021-04-10T16:07:05.849188Z",
     "iopub.status.idle": "2021-04-10T16:07:06.049023Z",
     "shell.execute_reply": "2021-04-10T16:07:06.049499Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    4.0\n",
       "2    0.5\n",
       "3   -3.0\n",
       "Name: a, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of a univariate series\n",
    "get_examples(\"pd.Series\", \"Series\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>-0.428571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a         b\n",
       "0  1.0  3.000000\n",
       "1  4.0  7.000000\n",
       "2  0.5  2.000000\n",
       "3 -3.0 -0.428571"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of a multivariate series\n",
    "get_examples(\"pd.DataFrame\", \"Series\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instances</th>\n",
       "      <th>timepoints</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      var_0  var_1\n",
       "instances timepoints              \n",
       "0         0               1      4\n",
       "          1               2      5\n",
       "          2               3      6\n",
       "1         0               1      4\n",
       "          1               2     55\n",
       "          2               3      6\n",
       "2         0               1     42\n",
       "          1               2      5\n",
       "          2               3      6"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of a panel with mtype pd-multiindex\n",
    "get_examples(\"pd-multiindex\", \"Panel\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[   var_0  var_1\n",
       " 0      1      4\n",
       " 1      2      5\n",
       " 2      3      6,\n",
       "    var_0  var_1\n",
       " 0      1      4\n",
       " 1      2     55\n",
       " 2      3      6,\n",
       "    var_0  var_1\n",
       " 0      1     42\n",
       " 1      2      5\n",
       " 2      3      6]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of the same panel with mtype df-list\n",
    "get_examples(\"df-list\", \"Panel\")[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sktime` supports more mtypes, see the \"datatypes and datasets\" tutorial for more details."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.2 General transformer signature - time series transformers<a class=\"anchor\" id=\"section_3_4_2\"></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transformers for `Series` and `Panel` have the same high-level interface. Depending which data type they are more commonly used for, they are found either in the `transformations.series` or `transformations.panel` module. As said, this does not imply a separate interface.\n",
    "\n",
    "The most important interface points of transformers are:\n",
    "\n",
    "1. construction with parameters, this is as with any other `sktime` estimator\n",
    "2. fitting the transformer, via `fit`\n",
    "3. transforming data, via `transform`\n",
    "4. inverse transforming, via `inverse_transform` - not all transformers have this interface point, since not all are invertible\n",
    "5. updating the transformer, via `update` - not all transformers have this interface point (`update` is currently work in progress, as of v0.8.x, contributions are appreciated)\n",
    "\n",
    "We show this using two example transformers below - one whose `transform` outputs `Series`, and one whose `transform` outputs primitive features (numbers or categories).\n",
    "\n",
    "We will apply both transformations to the following `Series` and `Panel` data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datatypes import get_examples\n",
    "\n",
    "# univariate series used in the examples\n",
    "X_series = get_examples(\"pd.Series\", \"Series\")[3]\n",
    "# panel used in the examples\n",
    "X_panel = get_examples(\"pd-multiindex\", \"Panel\")[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    4.0\n",
       "2    0.5\n",
       "3    3.0\n",
       "Name: a, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>var_0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instances</th>\n",
       "      <th>timepoints</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      var_0\n",
       "instances timepoints       \n",
       "0         0               4\n",
       "          1               5\n",
       "          2               6"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_panel"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: transforming series to series\n",
    "\n",
    "The Box-Cox transformer applies the Box-Cox transform to individual values in series or panels. At the start, the transformer needs to be constructed with parameter settings, this is the same as for any `sktime` estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing the transformer\n",
    "from sktime.transformations.series.boxcox import BoxCoxTransformer\n",
    "\n",
    "my_boxcox_trafo = BoxCoxTransformer(method=\"mle\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we apply the constructed transformer `my_trafo` to a (single, univariate) series. First, the transformer is fitted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 {color: black;background-color: white;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 pre{padding: 0;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-toggleable {background-color: white;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-estimator:hover {background-color: #d4ebff;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-item {z-index: 1;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-parallel-item:only-child::after {width: 0;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33 div.sk-text-repr-fallback {display: none;}</style><div id='sk-c8fbf582-f9f1-4e98-b36f-d2b2bf02aa33' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BoxCoxTransformer()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('46c19700-5532-4e22-8c6e-3c7437f8d235') type=\"checkbox\" checked><label for=UUID('46c19700-5532-4e22-8c6e-3c7437f8d235') class='sk-toggleable__label sk-toggleable__label-arrow'>BoxCoxTransformer</label><div class=\"sk-toggleable__content\"><pre>BoxCoxTransformer()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BoxCoxTransformer()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the transformer\n",
    "my_boxcox_trafo.fit(X_series)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the transformer is applied, this results in a transformed series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000000\n",
       "1    1.636217\n",
       "2   -0.640098\n",
       "3    1.251936\n",
       "Name: a, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transforming the series\n",
    "my_boxcox_trafo.transform(X_series)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, the series passed to `transform` need not be the same as in `fit`, but if they are the same, the shorthand `fit_transform` can instead be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000000\n",
       "1    1.636217\n",
       "2   -0.640098\n",
       "3    1.251936\n",
       "Name: a, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_boxcox_trafo.fit_transform(X_series)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The transformer can also be applied to `Panel` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>var_0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instances</th>\n",
       "      <th>timepoints</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>2.156835</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.702737</td>\n",
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       "      <th>2</th>\n",
       "      <td>3.206011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         var_0\n",
       "instances timepoints          \n",
       "0         0           2.156835\n",
       "          1           2.702737\n",
       "          2           3.206011"
      ]
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     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_boxcox_trafo.fit_transform(X_panel)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the `BoxCoxTransformer` used has applied the Box-Cox transform to every series in the panel individually,\n",
    "but that need not be the case transformers in general."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: transforming series to primitive features\n",
    "\n",
    "The summary transformer can be used to extract sample statistics such as mean and variance from a series. First, we construct the transformer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing the transformer\n",
    "from sktime.transformations.series.summarize import SummaryTransformer\n",
    "\n",
    "my_summary_trafo = SummaryTransformer()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we can fit/apply with `fit`, `transform`, and `fit_transform`.\n",
    "\n",
    "`SummaryTransformer` returns primitive features, hence the output will be a `pandas.DataFrame`, each row corresponding to one series in the input. \n",
    "\n",
    "If the input is a single series, the output of `transform` and `fit_transform` will be a one-row, nine-column `DataFrame`, corresponding to the nine-number-summary of that one series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
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       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>2.125</td>\n",
       "      <td>1.652019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.875</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean       std  min  max   0.1   0.25  0.5  0.75  0.9\n",
       "0  2.125  1.652019  0.5  4.0  0.65  0.875  2.0  3.25  3.7"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_summary_trafo.fit_transform(X_series)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the input is a panel, the output of `transform` and `fit_transform` will be a `DataFrame` with as many rows as the `Panel` had series. The `i`th row has the summary statistics of the `i`th series in the panel `X_panel`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instances</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean  std  min  max  0.1  0.25  0.5  0.75  0.9\n",
       "instances                                                \n",
       "0           5.0  1.0  4.0  6.0  4.2   4.5  5.0   5.5  5.8"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_summary_trafo.fit_transform(X_panel)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformers with series/panel output vs primitive output\n",
    "\n",
    "Whether `transform` will return time series like objects (`Series` or `Panel`) or primitives (i.e. a `pandas.DataFrame`) can be checked by using the `\"scitype:transform-output\"` tag. This is `\"Series\"` for behaviour as in the first example (`BoxCoxTransformer`), and `\"Primitives\"` for behaviour as in the second example (`SummaryTransformer`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Series'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_boxcox_trafo.get_tag(\"scitype:transform-output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Primitives'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_summary_trafo.get_tag(\"scitype:transform-output\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use of tags to characterize and search for transformers will be discussed in more detail in section 4."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: currently not all transformers are refactored to accept both `Series` to `Panel` arguments, the above may hence not fully work for all transformers. Contributions to the transformer refactor are very much appreciated."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.3 General transformer signature - pairwise series transformers<a class=\"anchor\" id=\"section_3_4_3\"></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pairwise series transformers model mathematical objects of signature `(Series, Series) -> float`, or, in mathematical notation, $$\\texttt{series} \\times\\texttt{series}\\rightarrow\\mathbb{R}$$\n",
    "Common examples are distances between series, or (positive definite) kernels on series.\n",
    "\n",
    "Pairwise transformers have a parametric constructor, like any other `sktime` object. The transformation is achieved by the method `transform`, or, equivalently, for brevity, by a call to the constructed object.\n",
    "\n",
    "The method `transform` always returns a 2D `numpy.ndarray`, and can be called in multiple ways:\n",
    "* with two `Series` arguments `X, X2`, in which case a 1 x 1 array is returned. Denote this function by `t(X, X2)`\n",
    "* with two `Panel` arguments `X`, `X2`, in which case an `m x n` array is returned, where `m` is the number of instances in `X` and `n` is the number of instances in `X2`. The `(i,j)`-th entry corresponds to `t(Xi, X2j)`, where `Xi` is the `i`-th `Series` in the `Panel` `X`, and `X2j` is the `j`-th `Series` in the `Panel` `X2`.\n",
    "* with one `Series` and one `Panel` argument, in which case the `Series` is interpreted as a 1-element `Panel`, with return as above.\n",
    "* with one single argument, `Series` or `Panel`, in which case `X` and `X2` are assumed to be the same as the one argument, with behaviour as above.\n",
    "\n",
    "We show these in a few examples below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datatypes import get_examples\n",
    "\n",
    "# unviariate series used in the examples\n",
    "X_series = get_examples(\"pd.Series\", \"Series\")[0]\n",
    "X2_series = get_examples(\"pd.Series\", \"Series\")[1]\n",
    "# panel used in the examples\n",
    "X_panel = get_examples(\"pd-multiindex\", \"Panel\")[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we construct the pairwise transformer with parameters. In this case, the pairwise transformer is a distance (the mean Euclidean distance):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing the transformer\n",
    "from sktime.dists_kernels import AggrDist, ScipyDist\n",
    "\n",
    "# mean of paired Euclidean distances\n",
    "my_series_dist = AggrDist(ScipyDist(metric=\"euclidean\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then evaluate the distance by `transform` or direct call:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.6875]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the metric on two series, via transform\n",
    "my_series_dist.transform(X_series, X2_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.6875]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the metric on two series, by direct call - this is the same\n",
    "my_series_dist(X_series, X2_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.25707872, 17.6116986 , 13.12667685],\n",
       "       [17.6116986 , 22.85520736, 21.30677498],\n",
       "       [13.12667685, 21.30677498, 16.55183053]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the metric on two identical panels of three series\n",
    "my_series_dist(X_panel, X_panel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.25707872, 17.6116986 , 13.12667685],\n",
       "       [17.6116986 , 22.85520736, 21.30677498],\n",
       "       [13.12667685, 21.30677498, 16.55183053]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the same as providing only one argument\n",
    "my_series_dist(X_panel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.375     , 21.04166667, 17.04166667]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one series, one panel\n",
    "# we subset X_panel to univariate, since the distance in question\n",
    "#     cannot compare series with different number of variables\n",
    "my_series_dist(X_series, X_panel[[\"var_1\"]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pairwise transformers are composable, and use the familiar `get_params` interface, just like any other `sktime` object and `scikit-learn` estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aggfunc': None,\n",
       " 'aggfunc_is_symm': False,\n",
       " 'transformer': ScipyDist(),\n",
       " 'transformer__colalign': 'intersect',\n",
       " 'transformer__metric': 'euclidean',\n",
       " 'transformer__metric_kwargs': None,\n",
       " 'transformer__p': 2,\n",
       " 'transformer__var_weights': None}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series_dist.get_params()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.4 General transformer signature - pairwise transformers<a class=\"anchor\" id=\"section_3_4_4\"></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sktime` also provides functionality for pairwise transformers on tabular data, i.e., mathematical objects of signature `(DataFrame-row, DataFrame-row) -> float`, or, in mathematical notation, $$\\mathbb{R}^n \\times\\mathbb{R}^n\\rightarrow\\mathbb{R}$$.\n",
    "Common examples are distances between series, or (positive definite) kernels on series.\n",
    "\n",
    "The behaviour is as for series transformers, evaluation is callable by `transform(X, X2)` or a direct call.\n",
    "\n",
    "Inputs to `transform` of a pairwise (tabular) transformer must always be `pandas.DataFrame`.\n",
    "The output is an `m x n` matrix, a 2D `np.ndarray`, with `m = len(X), n=len(X2)`. \n",
    "The `(i,j)`-th entry corresponds to `t(Xi, X2j)`, where `Xi` is the `i`-th row of `X`, and `X2j` is the `j`-th row of `X2`.\n",
    "If `X2` is not passed, it defaults to `X`.\n",
    "\n",
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datatypes import get_examples\n",
    "\n",
    "# we retrieve some DataFrame examples\n",
    "X_tabular = get_examples(\"pd.DataFrame\", \"Series\")[1]\n",
    "X2_tabular = get_examples(\"pd.DataFrame\", \"Series\")[1][0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing the transformer\n",
    "from sktime.dists_kernels import ScipyDist\n",
    "\n",
    "# mean of paired Euclidean distances\n",
    "my_tabular_dist = ScipyDist(metric=\"euclidean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  5.        ,  1.11803399],\n",
       "       [ 5.        ,  0.        ,  6.10327781],\n",
       "       [ 1.11803399,  6.10327781,  0.        ],\n",
       "       [ 5.26831112, 10.20704039,  4.26004216]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtain matrix of distances between each pair of rows in X_tabular, X2_tabular\n",
    "my_tabular_dist(X_tabular, X2_tabular)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.5 Searching for transformers<a class=\"anchor\" id=\"section_3_4_5\"></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with all `sktime` objects, we can use the `registry.all_estimators` utility to display all transformers in `sktime`.\n",
    "\n",
    "The relevant scitypes are:\n",
    "* `\"transformer\"` for all transformers (as in Section 2.2)\n",
    "* `\"transformer-pairwise\"` for all pairwise transformers on tabular data (as in Section 2.4)\n",
    "* `\"transformer-panel\"` for all pairwise transformers on panel data (as in Section 2.3)\n",
    "\n",
    "To filter transformers (`\"transformer\"` scitype) further by input and output, use tags, most importantly:\n",
    "* `\"scitype:transform-output\"` - the output scitype that the transform produces. `Series` for time series, `Primitives` for primitive features (float, categories).\n",
    "* `\"scitype:instancewise\"` - whether transform uses all samples or acts by instance. If `True`, this is simply a vectorized operation per series. If `False`, then fitting on a single series does not have the same result as fitting on multiple.\n",
    "\n",
    "These and further tags will be explained in more detail in Section 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.registry import all_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADICVTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.adi_cv.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aggregator</td>\n",
       "      <td>&lt;class 'sktime.transformations.hierarchical.ag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AutoCorrelationTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.acf.Auto...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BKFilter</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.bkfilter...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bollinger</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.bollinge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>TruncationTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.truncatio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>VmdTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.vmd.VmdT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>WhiteNoiseAugmenter</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.augmente...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>WindowSummarizer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.summariz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>YtoX</td>\n",
       "      <td>&lt;class 'sktime.transformations.compose._ytox.Y...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name  \\\n",
       "0              ADICVTransformer   \n",
       "1                    Aggregator   \n",
       "2    AutoCorrelationTransformer   \n",
       "3                      BKFilter   \n",
       "4                     Bollinger   \n",
       "..                          ...   \n",
       "123       TruncationTransformer   \n",
       "124              VmdTransformer   \n",
       "125         WhiteNoiseAugmenter   \n",
       "126            WindowSummarizer   \n",
       "127                        YtoX   \n",
       "\n",
       "                                                object  \n",
       "0    <class 'sktime.transformations.series.adi_cv.A...  \n",
       "1    <class 'sktime.transformations.hierarchical.ag...  \n",
       "2    <class 'sktime.transformations.series.acf.Auto...  \n",
       "3    <class 'sktime.transformations.series.bkfilter...  \n",
       "4    <class 'sktime.transformations.series.bollinge...  \n",
       "..                                                 ...  \n",
       "123  <class 'sktime.transformations.panel.truncatio...  \n",
       "124  <class 'sktime.transformations.series.vmd.VmdT...  \n",
       "125  <class 'sktime.transformations.series.augmente...  \n",
       "126  <class 'sktime.transformations.series.summariz...  \n",
       "127  <class 'sktime.transformations.compose._ytox.Y...  \n",
       "\n",
       "[128 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# listing all pairwise panel transformers - distances, kernels on time series\n",
    "all_estimators(\"transformer\", as_dataframe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADICVTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.adi_cv.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Catch22</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.catch22.C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Catch22Wrapper</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.catch22wr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DistanceFeatures</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.compose_d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FittedParamExtractor</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.summarize...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MatrixProfile</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.matrix_pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MiniRocket</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.rocket._m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MiniRocketMultivariate</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.rocket._m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MiniRocketMultivariateVariable</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.rocket._m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MultiRocket</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.rocket._m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MultiRocketMultivariate</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.rocket._m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RandomIntervalFeatureExtractor</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.summarize...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RandomIntervals</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.random_in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RandomShapeletTransform</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.shapelet_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rocket</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.rocket._r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RocketPyts</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.rocket._r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ShapeletTransform</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.shapelet_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ShapeletTransformPyts</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.shapelet_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SignatureTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.signature...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SummaryTransformer</td>\n",
       "      <td>&lt;class 'sktime.transformations.series.summariz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SupervisedIntervals</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.supervise...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TSFreshFeatureExtractor</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.tsfresh.T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TSFreshRelevantFeatureExtractor</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.tsfresh.T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tabularizer</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.reduce.Ta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TimeBinner</td>\n",
       "      <td>&lt;class 'sktime.transformations.panel.reduce.Ti...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name  \\\n",
       "0                  ADICVTransformer   \n",
       "1                           Catch22   \n",
       "2                    Catch22Wrapper   \n",
       "3                  DistanceFeatures   \n",
       "4              FittedParamExtractor   \n",
       "5                     MatrixProfile   \n",
       "6                        MiniRocket   \n",
       "7            MiniRocketMultivariate   \n",
       "8    MiniRocketMultivariateVariable   \n",
       "9                       MultiRocket   \n",
       "10          MultiRocketMultivariate   \n",
       "11   RandomIntervalFeatureExtractor   \n",
       "12                  RandomIntervals   \n",
       "13          RandomShapeletTransform   \n",
       "14                           Rocket   \n",
       "15                       RocketPyts   \n",
       "16                ShapeletTransform   \n",
       "17            ShapeletTransformPyts   \n",
       "18             SignatureTransformer   \n",
       "19               SummaryTransformer   \n",
       "20              SupervisedIntervals   \n",
       "21          TSFreshFeatureExtractor   \n",
       "22  TSFreshRelevantFeatureExtractor   \n",
       "23                      Tabularizer   \n",
       "24                       TimeBinner   \n",
       "\n",
       "                                               object  \n",
       "0   <class 'sktime.transformations.series.adi_cv.A...  \n",
       "1   <class 'sktime.transformations.panel.catch22.C...  \n",
       "2   <class 'sktime.transformations.panel.catch22wr...  \n",
       "3   <class 'sktime.transformations.panel.compose_d...  \n",
       "4   <class 'sktime.transformations.panel.summarize...  \n",
       "5   <class 'sktime.transformations.panel.matrix_pr...  \n",
       "6   <class 'sktime.transformations.panel.rocket._m...  \n",
       "7   <class 'sktime.transformations.panel.rocket._m...  \n",
       "8   <class 'sktime.transformations.panel.rocket._m...  \n",
       "9   <class 'sktime.transformations.panel.rocket._m...  \n",
       "10  <class 'sktime.transformations.panel.rocket._m...  \n",
       "11  <class 'sktime.transformations.panel.summarize...  \n",
       "12  <class 'sktime.transformations.panel.random_in...  \n",
       "13  <class 'sktime.transformations.panel.shapelet_...  \n",
       "14  <class 'sktime.transformations.panel.rocket._r...  \n",
       "15  <class 'sktime.transformations.panel.rocket._r...  \n",
       "16  <class 'sktime.transformations.panel.shapelet_...  \n",
       "17  <class 'sktime.transformations.panel.shapelet_...  \n",
       "18  <class 'sktime.transformations.panel.signature...  \n",
       "19  <class 'sktime.transformations.series.summariz...  \n",
       "20  <class 'sktime.transformations.panel.supervise...  \n",
       "21  <class 'sktime.transformations.panel.tsfresh.T...  \n",
       "22  <class 'sktime.transformations.panel.tsfresh.T...  \n",
       "23  <class 'sktime.transformations.panel.reduce.Ta...  \n",
       "24  <class 'sktime.transformations.panel.reduce.Ti...  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now subset to transformers that extract scalar features\n",
    "all_estimators(\n",
    "    \"transformer\",\n",
    "    as_dataframe=True,\n",
    "    filter_tags={\"scitype:transform-output\": \"Primitives\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ScipyDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.scipy_dist.ScipyD...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name                                             object\n",
       "0  ScipyDist  <class 'sktime.dists_kernels.scipy_dist.ScipyD..."
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     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# listing all pairwise (tabular) transformers - distances, kernels on vectors/df-rows\n",
    "all_estimators(\"transformer-pairwise\", as_dataframe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AggrDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.compose_tab_to_pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CombinedDistance</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.algebra.CombinedD...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ConstantPwTrafoPanel</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dummy.ConstantPwT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CtwDistTslearn</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.ctw.CtwDistTslearn'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DistFromAligner</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.compose_from_alig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DistFromKernel</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dist_to_kern.Dist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DtwDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dtw._dtw_sktime.D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DtwDistTslearn</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dtw._dtw_tslearn....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DtwDtaidistMultiv</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dtw._dtw_dtaidist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DtwDtaidistUniv</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dtw._dtw_dtaidist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DtwPythonDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dtw._dtw_python.D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EditDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.edit_dist.EditDist'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FlatDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.compose_tab_to_pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GAKernel</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.gak.GAKernel'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IndepDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.indep.IndepDist'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KernelFromDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dist_to_kern.Kern...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LcssTslearn</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.lcss.LcssTslearn'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LuckyDtwDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.lucky.LuckyDtwDist'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PwTrafoPanelPipeline</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.compose.PwTrafoPa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SignatureKernel</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.signature_kernel....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SoftDtwDistTslearn</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dtw._dtw_tslearn....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name                                             object\n",
       "0               AggrDist  <class 'sktime.dists_kernels.compose_tab_to_pa...\n",
       "1       CombinedDistance  <class 'sktime.dists_kernels.algebra.CombinedD...\n",
       "2   ConstantPwTrafoPanel  <class 'sktime.dists_kernels.dummy.ConstantPwT...\n",
       "3         CtwDistTslearn  <class 'sktime.dists_kernels.ctw.CtwDistTslearn'>\n",
       "4        DistFromAligner  <class 'sktime.dists_kernels.compose_from_alig...\n",
       "5         DistFromKernel  <class 'sktime.dists_kernels.dist_to_kern.Dist...\n",
       "6                DtwDist  <class 'sktime.dists_kernels.dtw._dtw_sktime.D...\n",
       "7         DtwDistTslearn  <class 'sktime.dists_kernels.dtw._dtw_tslearn....\n",
       "8      DtwDtaidistMultiv  <class 'sktime.dists_kernels.dtw._dtw_dtaidist...\n",
       "9        DtwDtaidistUniv  <class 'sktime.dists_kernels.dtw._dtw_dtaidist...\n",
       "10         DtwPythonDist  <class 'sktime.dists_kernels.dtw._dtw_python.D...\n",
       "11              EditDist  <class 'sktime.dists_kernels.edit_dist.EditDist'>\n",
       "12              FlatDist  <class 'sktime.dists_kernels.compose_tab_to_pa...\n",
       "13              GAKernel        <class 'sktime.dists_kernels.gak.GAKernel'>\n",
       "14             IndepDist     <class 'sktime.dists_kernels.indep.IndepDist'>\n",
       "15        KernelFromDist  <class 'sktime.dists_kernels.dist_to_kern.Kern...\n",
       "16           LcssTslearn    <class 'sktime.dists_kernels.lcss.LcssTslearn'>\n",
       "17          LuckyDtwDist  <class 'sktime.dists_kernels.lucky.LuckyDtwDist'>\n",
       "18  PwTrafoPanelPipeline  <class 'sktime.dists_kernels.compose.PwTrafoPa...\n",
       "19       SignatureKernel  <class 'sktime.dists_kernels.signature_kernel....\n",
       "20    SoftDtwDistTslearn  <class 'sktime.dists_kernels.dtw._dtw_tslearn...."
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# listing all pairwise panel transformers - distances, kernels on time series\n",
    "all_estimators(\"transformer-pairwise-panel\", as_dataframe=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Extension guide - implementing your own transformer <a class=\"anchor\" id=\"section_3_5\"></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sktime` is meant to be easily extensible, for direct contribution to `sktime` as well as for local/private extension with custom methods.\n",
    "\n",
    "To extend `sktime` with a new local or contributed transformer, a good workflow to follow is:\n",
    "\n",
    "1. read through the [transformer extension template](https://github.com/alan-turing-institute/sktime/blob/main/extension_templates/transformer.py) - this is a `python` file with `todo` blocks that mark the places in which changes need to be added.\n",
    "2. optionally, if you are planning any major surgeries to the interface: look at the [base class architecture](https://github.com/alan-turing-institute/sktime/blob/main/sktime/transformations/base.py) - note that \"ordinary\" extension (e.g., new algorithm) should be easily doable without this.\n",
    "3. copy the transformer extension template to a local folder in your own repository (local/private extension), or to a suitable location in your clone of the `sktime` or affiliated repository (if contributed extension), inside `sktime.transformations`; rename the file and update the file docstring appropriately.\n",
    "4. address the \"todo\" parts. Usually, this means: changing the name of the class, setting the tag values, specifying hyper-parameters, filling in `__init__`, `_fit`, `_transform`, and optional methods such as `_inverse_transform` or `_update` (for details see the extension template). You can add private methods as long as they do not override the default public interface. For more details, see the extension template.\n",
    "5. to test your estimator manually: import your estimator and run it in the workflows in Section 2.2; then use it in the compositors in Section 2.3.\n",
    "6. to test your estimator automatically: call `sktime.tests.test_all_estimators.check_estimator` on your estimator. You can call this on a class or object instance. Ensure you have specified test parameters in the `get_test_params` method, according to the extension template.\n",
    "\n",
    "In case of direct contribution to `sktime` or one of its affiliated packages, additionally:\n",
    "* Add yourself as an author and/or a maintainer for the new estimator file(s), via `\"authors\"` and `\"maintainers\"` tag.\n",
    "* create a pull request that contains only the new estimators (and their inheritance tree, if it's not just one class), as well as the automated tests as described above.\n",
    "* in the pull request, describe the estimator and optimally provide a publication or other technical reference for the strategy it implements.\n",
    "* before making the pull request, ensure that you have all necessary permissions to contribute the code to a permissive license (BSD-3) open source project."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 Summary <a class=\"anchor\" id=\"section_3_6\"></a>\n",
    "\n",
    "* transformers are data processing steps with unified interface - `fit`, `transform`, and optional `inverse_transform`\n",
    "\n",
    "* used as pipeline components for any learning task, forecasting, classification\n",
    "\n",
    "* different types by input/output - time series, primitives, pairs of time series, panels/hierarchical.\n",
    "\n",
    "* find transformers by tags such as `scitype:transform-output` and `scitype:instancewise` using `all_estimators`\n",
    "\n",
    "* rich composition syntax - `*` for pipe, `+` for featureunion, `[in, out]` for variable subset, `|` for multiplex/switch\n",
    "\n",
    "* `sktime` provides easy-to-use extension templates for transformers, build your own, plug and play"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Credits: notebook 3 - transformers\n",
    "\n",
    "notebook creation: fkiraly\n",
    "\n",
    "transformer pipelines & compositors: fkiraly, mloning, miraep8\\\n",
    "forecaster pipelines: fkiraly, aiwalter\\\n",
    "classifier/regressor pipelines: fkiraly\\\n",
    "transformer base interface: mloning, fkiraly\\\n",
    "dunder interface: fkiraly, miraep8\n",
    "\n",
    "Based on design ideas: sklearn, magrittr, mlr, mlj"
   ]
  }
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